We have recently released our systematic review score, we’ve had our clinical guideline score for ages and we’re exploring an RCT score.

In the spirit of transparency, wanting to encourage discussion and ultimately improve things I have posted an email I sent to the Evidence-Based Health discussion list. Hopefully it’s self-explanatory!

Hi All,

I recently asked for comments on a possible scoring system for RCTs (following on from our recent systematic review scoring system) and I ended up having a really interesting discussion with someone not on this mail-list (it was forwarded to him by someone on this list).  It was quite long and so I asked ChatGPT to summarise the respective positions (that’s point 1 below). I shared the summary with the respondent and his comments are point 2. Finally, point 3 is a possible enhancement on the scoring system, asking ChatGPT to critique a study (there are two examples).

To reiterate, my motivation for introducing a scoring system is to try to ensure users (of the Trip Database) do not see a systematic review or RCT and assume – given their privileged position in EBM – to be ‘good’ evidence. To me, the scores are an attempt to induce scepticism (although I do think the ChatGPT critiques are at a different level).  I really would be interested in what the collective wisdom of the list makes of this and possible ways I could enhance the way these scores are framed to ensure they have maximal effect.

Thanks in advance.

jon

1) LLM Summary of debate

Main Points of Concern:

1. Oversimplification of Complex Issues:
   • Reducing complex study design elements (e.g., blinding, sample size) to numeric scores risks missing important nuances. Some study flaws may not be easily quantifiable, leading to a misleading or oversimplified evaluation.
2. Subjectivity in Scoring:
   • Numeric scores are often subjective and can be arbitrary (e.g., how to weight factors like sample size or blinding). This subjectivity risks distorting the assessment of study quality.
3. Undermining Critical Thinking:
   • A scoring system could discourage users from critically engaging with the study. It might promote the idea that a score is enough, instead of encouraging users to examine the study’s methodology in depth.
4. LLMs and Critical Appraisal:
   • While LLMs can flag potential issues, they don’t provide a nuanced, complete critique of a study, and may not capture critical flaws or explain them in a meaningful way.

Counter Arguments (Your Position):

1. Better Than Doing Nothing:
   • A scoring system offers a framework for users to begin evaluating study quality, providing a starting point, especially for those without advanced knowledge in study design. It can guide further investigation rather than replace critical thinking.
2. Encouraging Engagement:
   • Even if simplified, the score encourages users to think about study quality. It can prompt users to dive deeper into flagged issues and learn more about study design, bias, and other important factors.
3. Making Information Accessible:
   • A scoring system simplifies complex information and makes it more digestible, particularly for non-experts. It helps make the evaluation of studies more accessible without overwhelming users with technical details.
4. Practical Decision-Making Tool:
   • In practical settings, especially in healthcare, having a quick, accessible tool for assessing study quality is better than having no tool at all. It saves time and allows for quicker decision-making when reviewing large volumes of studies or systematic reviews.

2) Commentators Reflection on the summary

The summary you sent is not bad … but it feels to me like it suffers in much the same way that the scoring system would suffer — it’s a little too superficial, and doesn’t explain the criticisms it makes.  (It mentions the word “nuanced” several times … but it itself lacks depth, and thus also nuance.)  Still, I’m not willing to write a long essay about this … which is what would really be needed. 

3) LLM Supported Critical Appraisal

Example One: Treating asthma patients with probiotics: a systematic review and meta-analysis

Summary (which we show when they make the initial call)

This meta-analysis of 10 randomized controlled trials involving 1,101 asthma patients found that probiotics significantly improved lung inflammation (FeNO), symptom severity, CACT scores, and reduced acute asthma episodes. However, no significant improvements were found in lung function (FEV1 and FEV1/FVC). The study suggests that probiotics may be useful as an adjunct treatment for asthma, especially for reducing symptoms and inflammation, but not for improving lung function. Further research is needed to explore the long-term safety and effectiveness of probiotics in asthma.

Extended analysis (user would have to press ‘More’ to be shown it)

Study Design:

• Meta-Analysis of Randomized Controlled Trials (RCTs): The study is a meta-analysis, which combines data from multiple RCTs to provide a more comprehensive understanding of the effect of probiotics on asthma. RCTs are considered the gold standard in clinical research due to their ability to minimize bias.

Sample Size and Power:

• Total Sample Size: The meta-analysis includes 1,101 participants across 10 RCTs, which provides a reasonable sample size to detect significant effects and increase the generalizability of the findings.
• Power: The sample size from the combined trials appears large enough to detect meaningful differences in asthma-related outcomes.

Outcome Measures:

• Primary Outcomes: The meta-analysis assessed several outcomes related to asthma treatment:
  • Fractional exhaled nitric oxide (FeNO): Used as a marker of airway inflammation. The probiotics group showed a significant reduction (MD = -7.17, 95% CI: -12.81, -1.54).
  • Asthma symptom severity: The probiotics group showed improvement (MD = -0.07, 95% CI: -0.10, -0.04).
  • Childhood Asthma Control Test (CACT): The probiotics group had a significant improvement (MD = 2.26, 95% CI: 1.14, 3.39).
  • Number of acute episodes of asthma: The probiotics group showed a reduced rate of acute episodes (OR = 0.30, 95% CI: 0.19, 0.47).
• Secondary Outcomes:
  • Forced expiratory volume in the first second (FEV1) and FEV1/FVC (%): No significant difference between probiotics and control groups (MD = 0.11, 95% CI: -0.05, 0.26 for FEV1 and MD = 0.32, 95% CI: -1.48, 2.12 for FEV1/FVC).

Statistical Methods:

• Meta-analysis: The meta-analysis used Revman 5.4 software and combined the effect sizes from the individual studies using odds ratio (OR) and mean difference (MD), with 95% confidence intervals (CI). These are appropriate statistical methods for synthesizing data across multiple studies.
• Significance: The significant improvements in FeNO, symptom severity, CACT, and acute episodes all had narrow confidence intervals, indicating the robustness of the findings. However, no significant changes were found in lung function (FEV1 and FEV1/FVC), suggesting that probiotics may not impact lung function directly.

Bias and Validity:

• Inclusion Criteria: The meta-analysis included only randomized controlled trials (RCTs), which reduces the risk of bias. However, it is important to consider whether all included RCTs had proper blinding and allocation concealment.
• Publication Bias: The abstract does not mention whether a funnel plot or other method was used to assess publication bias, which could affect the validity of the findings.
• Heterogeneity: The abstract does not mention whether there was significant heterogeneity between the studies, which would be important to assess the consistency of the results across the included trials.

Safety and Adverse Events:

• Adverse Events: The abstract does not mention any adverse events associated with probiotics. While probiotics are generally considered safe, any potential side effects or concerns about long-term use should be addressed in the full study.

Conflicts of Interest:

• Not Reported: The abstract does not provide information on potential conflicts of interest or funding sources, which would help assess the potential for bias in the conduct or reporting of the meta-analysis.

Conclusions and Applicability:

• Conclusions: The meta-analysis suggests that probiotics can improve lung inflammation, reduce asthma symptoms, and decrease the frequency of asthma attacks in patients with asthma. However, probiotics did not affect lung function (FEV1 and FEV1/FVC), which indicates that their effects may be limited to reducing inflammation and symptoms rather than improving airflow.
• Clinical Applicability: The findings support the use of probiotics as an adjunctive treatment for asthma, particularly for improving symptoms and reducing acute episodes. However, the lack of effect on lung function means that probiotics should not be considered a replacement for conventional asthma treatments, such as inhalers or corticosteroids.

Example Two: Benralizumab improves symptoms of patients with severe, eosinophilic asthma with a diagnosis of nasal polyposis

Summary (which we show when they make the initial call)

This randomized controlled trial of benralizumab in patients with severe, eosinophilic asthma and nasal polyposis showed significant improvements in SNOT-22 scores, asthma outcomes, and reduced exacerbations compared to placebo. While adverse events were similar between groups, no serious safety concerns were raised. However, the analysis was post-hoc, and nominal p-values were used, which limits the strength of the conclusions. The study’s small sample size in the NP substudy and the lack of information on conflicts of interest or blinding are additional considerations. The findings suggest that benralizumab may be a promising treatment for this patient population, but further research is needed to confirm these results.

Extended analysis (user would have to press ‘More’ to be shown it)

Study Design:

• Randomized Controlled Trial (RCT): This design helps ensure that the results are due to the intervention (benralizumab) and not other factors. The random allocation of participants helps minimize selection bias.
• Post-hoc Analysis: This is a secondary analysis of a previously conducted trial (ANDHI), which may introduce bias due to non-randomization for the specific analysis and can limit the strength of conclusions.

Sample Size and Power:

• Sample Size: Of the 656 total participants in the ANDHI trial, 153 patients participated in the NP substudy (96 in the benralizumab group and 57 in the placebo group). The sample size for the NP substudy is relatively small, particularly in the placebo group, which may limit statistical power and generalizability of results.
• Power Consideration: The study does not explicitly mention a sample size calculation for the NP substudy, which makes it difficult to assess if the study is adequately powered to detect meaningful differences.

Outcome Measures:

• Primary Outcome: Improvement in Sino-Nasal Outcome Test-22 (SNOT-22) scores, with an improvement of at least 8.9 defined as clinically significant.
• Secondary Outcomes: Several asthma efficacy measures, including:
  • Annualized asthma exacerbation rate (AER)
  • St. George’s Respiratory Questionnaire (SGRQ) total score
  • Forced expiratory volume in one second (FEV1)
  • Asthma Control Questionnaire-6 (ACQ-6)
• Results: Benralizumab was associated with significant improvement in all measured outcomes (SNOT-22, AER, FEV1, SGRQ, ACQ-6).

Statistical Methods:

• P-values: Nominal p-values were reported for primary and secondary outcomes, and all findings related to benralizumab treatment showed statistical significance, including p-values of 0.0176 for SNOT-22 and <0.0001 for AER, FEV1, and SGRQ.
• Effect Size: The study reports percentages of responders and magnitude of change in the outcomes (e.g., 69% reduction in AER, +0.32 L change in FEV1).
• Nominal p-values: This is a limitation because without proper adjustment for multiple comparisons, the p-values could be inflated, and the findings might not be robust.

Bias and Validity:

• Selection Bias: Randomization helps reduce selection bias, but the post-hoc nature of the analysis could lead to selective reporting and influence the findings.
• Blinding: The abstract does not mention blinding, which could potentially lead to performance or detection bias if the outcomes are measured subjectively.
• Generalizability: The study population had severe, eosinophilic asthma and NP, and findings may not generalize to other asthma subtypes or patients without NP.

Safety and Adverse Events:

• Adverse Events: The frequency of adverse events (AEs) was similar between the benralizumab (76%) and placebo (73.7%) groups, indicating no significant safety concerns between groups.
  • Most common AEs reported in benralizumab group: headache, sinusitis, pyrexia, and influenza.
• No serious safety issues were identified, but the study does not report any severe or life-threatening adverse events, which would be important for understanding the full safety profile.

Conflicts of Interest:

• Not Reported: The abstract does not mention any potential conflicts of interest or funding sources. This information would help determine if there is any financial interest influencing the results.

Conclusions and Applicability:

• Effectiveness: Benralizumab showed clinically meaningful improvements in both nasal polyposis (SNOT-22) and asthma outcomes (AER, FEV1, SGRQ, ACQ-6).
• Clinical Applicability: These results suggest that benralizumab could be an effective treatment for patients with severe, eosinophilic asthma and nasal polyposis. However, the findings are based on a substudy of a larger trial and require confirmation in larger, more diverse populations before broad clinical implementation.
• Future Research: The conclusion indicates that further studies are needed to confirm these findings and establish broader applicability

Now our systematic review score has been released we’re exploring trying a similar approach to RCTs. The rationale for the RCT score is the same as the systematic review score – to encourage healthy scepticism among our users!

We are generating a list of potential criteria to score the RCT (it’ll need to be in the abstract or the metadata) and here is the initial list:

• Age
• Size
• Trial registry
• Funding
• Trial phrase
• Intention to treat
• Absolute versus relative effects/risks
• Blinding
• Citations
• Conflict of interest

What have we missed?

Another possibility is to use the above score as a simple guide (for all RCTs) and then allow users to ask for more in-depth analysis (using LLMs such as ChatGPT). Here’s an example of what information the LLM could extract, based on two different trials:

Example One: Treating asthma patients with probiotics: a systematic review and meta-analysis

Summary (which we show when they make the initial call)

This meta-analysis of 10 randomized controlled trials involving 1,101 asthma patients found that probiotics significantly improved lung inflammation (FeNO), symptom severity, CACT scores, and reduced acute asthma episodes. However, no significant improvements were found in lung function (FEV1 and FEV1/FVC). The study suggests that probiotics may be useful as an adjunct treatment for asthma, especially for reducing symptoms and inflammation, but not for improving lung function. Further research is needed to explore the long-term safety and effectiveness of probiotics in asthma.

Extended analysis (user would have to press ‘More’ to be shown it)

Study Design:

• Meta-Analysis of Randomized Controlled Trials (RCTs): The study is a meta-analysis, which combines data from multiple RCTs to provide a more comprehensive understanding of the effect of probiotics on asthma. RCTs are considered the gold standard in clinical research due to their ability to minimize bias.

Sample Size and Power:

• Total Sample Size: The meta-analysis includes 1,101 participants across 10 RCTs, which provides a reasonable sample size to detect significant effects and increase the generalizability of the findings.
• Power: The sample size from the combined trials appears large enough to detect meaningful differences in asthma-related outcomes.

Outcome Measures:

• Primary Outcomes: The meta-analysis assessed several outcomes related to asthma treatment:
  • Fractional exhaled nitric oxide (FeNO): Used as a marker of airway inflammation. The probiotics group showed a significant reduction (MD = -7.17, 95% CI: -12.81, -1.54).
  • Asthma symptom severity: The probiotics group showed improvement (MD = -0.07, 95% CI: -0.10, -0.04).
  • Childhood Asthma Control Test (CACT): The probiotics group had a significant improvement (MD = 2.26, 95% CI: 1.14, 3.39).
  • Number of acute episodes of asthma: The probiotics group showed a reduced rate of acute episodes (OR = 0.30, 95% CI: 0.19, 0.47).
• Secondary Outcomes:
  • Forced expiratory volume in the first second (FEV1) and FEV1/FVC (%): No significant difference between probiotics and control groups (MD = 0.11, 95% CI: -0.05, 0.26 for FEV1 and MD = 0.32, 95% CI: -1.48, 2.12 for FEV1/FVC).

Statistical Methods:

• Meta-analysis: The meta-analysis used Revman 5.4 software and combined the effect sizes from the individual studies using odds ratio (OR) and mean difference (MD), with 95% confidence intervals (CI). These are appropriate statistical methods for synthesizing data across multiple studies.
• Significance: The significant improvements in FeNO, symptom severity, CACT, and acute episodes all had narrow confidence intervals, indicating the robustness of the findings. However, no significant changes were found in lung function (FEV1 and FEV1/FVC), suggesting that probiotics may not impact lung function directly.

Bias and Validity:

• Inclusion Criteria: The meta-analysis included only randomized controlled trials (RCTs), which reduces the risk of bias. However, it is important to consider whether all included RCTs had proper blinding and allocation concealment.
• Publication Bias: The abstract does not mention whether a funnel plot or other method was used to assess publication bias, which could affect the validity of the findings.
• Heterogeneity: The abstract does not mention whether there was significant heterogeneity between the studies, which would be important to assess the consistency of the results across the included trials.

Safety and Adverse Events:

• Adverse Events: The abstract does not mention any adverse events associated with probiotics. While probiotics are generally considered safe, any potential side effects or concerns about long-term use should be addressed in the full study.

Conflicts of Interest:

• Not Reported: The abstract does not provide information on potential conflicts of interest or funding sources, which would help assess the potential for bias in the conduct or reporting of the meta-analysis.

Conclusions and Applicability:

• Conclusions: The meta-analysis suggests that probiotics can improve lung inflammation, reduce asthma symptoms, and decrease the frequency of asthma attacks in patients with asthma. However, probiotics did not affect lung function (FEV1 and FEV1/FVC), which indicates that their effects may be limited to reducing inflammation and symptoms rather than improving airflow.
• Clinical Applicability: The findings support the use of probiotics as an adjunctive treatment for asthma, particularly for improving symptoms and reducing acute episodes. However, the lack of effect on lung function means that probiotics should not be considered a replacement for conventional asthma treatments, such as inhalers or corticosteroids.

Example Two: Benralizumab improves symptoms of patients with severe, eosinophilic asthma with a diagnosis of nasal polyposis

Summary (which we show when they make the initial call)

This randomized controlled trial of benralizumab in patients with severe, eosinophilic asthma and nasal polyposis showed significant improvements in SNOT-22 scores, asthma outcomes, and reduced exacerbations compared to placebo. While adverse events were similar between groups, no serious safety concerns were raised. However, the analysis was post-hoc, and nominal p-values were used, which limits the strength of the conclusions. The study’s small sample size in the NP substudy and the lack of information on conflicts of interest or blinding are additional considerations. The findings suggest that benralizumab may be a promising treatment for this patient population, but further research is needed to confirm these results.

Extended analysis (user would have to press ‘More’ to be shown it)

Study Design:

• Randomized Controlled Trial (RCT): This design helps ensure that the results are due to the intervention (benralizumab) and not other factors. The random allocation of participants helps minimize selection bias.
• Post-hoc Analysis: This is a secondary analysis of a previously conducted trial (ANDHI), which may introduce bias due to non-randomization for the specific analysis and can limit the strength of conclusions.

Sample Size and Power:

• Sample Size: Of the 656 total participants in the ANDHI trial, 153 patients participated in the NP substudy (96 in the benralizumab group and 57 in the placebo group). The sample size for the NP substudy is relatively small, particularly in the placebo group, which may limit statistical power and generalizability of results.
• Power Consideration: The study does not explicitly mention a sample size calculation for the NP substudy, which makes it difficult to assess if the study is adequately powered to detect meaningful differences.

Outcome Measures:

• Primary Outcome: Improvement in Sino-Nasal Outcome Test-22 (SNOT-22) scores, with an improvement of at least 8.9 defined as clinically significant.
• Secondary Outcomes: Several asthma efficacy measures, including:
  • Annualized asthma exacerbation rate (AER)
  • St. George’s Respiratory Questionnaire (SGRQ) total score
  • Forced expiratory volume in one second (FEV1)
  • Asthma Control Questionnaire-6 (ACQ-6)
• Results: Benralizumab was associated with significant improvement in all measured outcomes (SNOT-22, AER, FEV1, SGRQ, ACQ-6).

Statistical Methods:

• P-values: Nominal p-values were reported for primary and secondary outcomes, and all findings related to benralizumab treatment showed statistical significance, including p-values of 0.0176 for SNOT-22 and <0.0001 for AER, FEV1, and SGRQ.
• Effect Size: The study reports percentages of responders and magnitude of change in the outcomes (e.g., 69% reduction in AER, +0.32 L change in FEV1).
• Nominal p-values: This is a limitation because without proper adjustment for multiple comparisons, the p-values could be inflated, and the findings might not be robust.

Bias and Validity:

• Selection Bias: Randomization helps reduce selection bias, but the post-hoc nature of the analysis could lead to selective reporting and influence the findings.
• Blinding: The abstract does not mention blinding, which could potentially lead to performance or detection bias if the outcomes are measured subjectively.
• Generalizability: The study population had severe, eosinophilic asthma and NP, and findings may not generalize to other asthma subtypes or patients without NP.

Safety and Adverse Events:

• Adverse Events: The frequency of adverse events (AEs) was similar between the benralizumab (76%) and placebo (73.7%) groups, indicating no significant safety concerns between groups.
  • Most common AEs reported in benralizumab group: headache, sinusitis, pyrexia, and influenza.
• No serious safety issues were identified, but the study does not report any severe or life-threatening adverse events, which would be important for understanding the full safety profile.

Conflicts of Interest:

• Not Reported: The abstract does not mention any potential conflicts of interest or funding sources. This information would help determine if there is any financial interest influencing the results.

Conclusions and Applicability:

• Effectiveness: Benralizumab showed clinically meaningful improvements in both nasal polyposis (SNOT-22) and asthma outcomes (AER, FEV1, SGRQ, ACQ-6).
• Clinical Applicability: These results suggest that benralizumab could be an effective treatment for patients with severe, eosinophilic asthma and nasal polyposis. However, the findings are based on a substudy of a larger trial and require confirmation in larger, more diverse populations before broad clinical implementation.
• Future Research: The conclusion indicates that further studies are needed to confirm these findings and establish broader applicability

To me, this appears massively helpful. It may be just as a way to help people structure their critical thinking around a particular article.

As ever let me know what you think!

Trip was started due to our work with clinical Q&A and started with the ATTRACT project. Since then Trip has worked on multiple separate Q&A projects and those teams have answered over 10,000 clinical questions. Our experience is unrivalled!

Within Trip we link to thousands of clinical Q&As but the vast majority are over ten years old so likely to be out of date. However, these are real clinical questions, most are likely to be relevant today and could be a valuable resource.

So, in testing our automated Q&A system, we thought we would update a random clinical question: A 43 year old lady wanting to conceive, is currently taking amlodipine and losartan for hypertension. Is this safe if she was to get pregnant whilst taking them and what is the alternative ‘safe’ antihypertensive for pregnancy? Clicking on the document will let you see the original answer from 2013.

I’m not sure how long the original answer would have taken, I would estimate 3-4 hours. Well, our updated answer took 90 seconds and – as we operationalise things – could take 10-15 seconds.

Oh yes, the new answer:

Evidence from extracted articles:

1. Losartan (ARB):
   • The Hypertension in Pregnancy Guideline (2023) states that angiotensin receptor blockers (ARBs), such as losartan, are contraindicated in pregnancy due to the risk of fetal malformations, including renal dysfunction, oligohydramnios, and skull hypoplasia.
   • The Management of Postpartum Hypertensive Disorders of Pregnancy document reinforces this, confirming that losartan should be discontinued before conception or as soon as pregnancy is confirmed.
   • The FDA and European Medicines Agency (EMA) classify losartan as pregnancy category D, meaning there is evidence of fetal risk, and it should be avoided during pregnancy.
   • Reference: FDA Pregnancy Categories (Note: The FDA has moved away from letter categories, but losartan remains contraindicated in pregnancy.)
2. Amlodipine (Calcium Channel Blocker):
   • The Pharmacokinetics of Common Antihypertensive Drugs in Pregnancy (2022) review notes that calcium channel blockers, including amlodipine, require further research to establish their safety profile in pregnancy.
   • A systematic review (2024) comparing amlodipine and nifedipine suggests that nifedipine is the preferred option due to more established safety data.
   • While amlodipine is not explicitly contraindicated, nifedipine (another calcium channel blocker) has a stronger safety profile for pregnancy-related hypertension and is often preferred.
   • Reference: Pharmacokinetics of Antihypertensive Drugs in Pregnancy (Note: Specific link to the 2022 review is not provided, but this is a general reference for further reading.)

---

What are the alternative ‘safe’ antihypertensive options for pregnancy?

Evidence from extracted articles:

1. The Hypertension in Pregnancy Guideline (2023) recommends the following as first-line treatments for hypertension in pregnancy:
   • Labetalol (a beta-blocker)
   • Nifedipine (extended-release, a calcium channel blocker)
   • Methyldopa (a centrally acting alpha-agonist)
2. The systematic review on pharmacokinetics of antihypertensive drugs (2022) confirms that methyldopa, labetalol, and nifedipine are generally safe options for pregnancy.

Additional knowledge from clinical guidelines (outside extracted articles):

• Labetalol is often the first-line choice due to its efficacy, safety, and minimal fetal side effects.
• Nifedipine is used when labetalol is not suitable or contraindicated.
• Methyldopa, though safe, is less commonly used due to side effects such as fatigue, depression, and drowsiness.

References:

• Hypertension in Pregnancy Guidelines (2023) (Note: Replace with the actual link to the 2023 guideline if available.)
• NICE Guidelines on Hypertension in Pregnancy

---

Final Clinical Recommendations:

1. Losartan should be discontinued before conception or immediately upon confirming pregnancy due to the risk of fetal harm.
2. Amlodipine is not absolutely contraindicated, but nifedipine is preferred as a safer calcium channel blocker during pregnancy.
3. First-line alternatives for managing hypertension in pregnancy include:
   • Labetalol (most commonly recommended)
   • Nifedipine (extended-release)
   • Methyldopa (less preferred due to side effects)

---

Additional Notes:

• If the patient is planning to conceive, it is crucial to switch from losartan to a safer antihypertensive before pregnancy.
• Regular monitoring of blood pressure and fetal growth is essential during pregnancy.
• Always consult a healthcare provider for personalized advice and management.

In our previous post we used AI to prioritise research priorities for Parkinson’s Disease. But how do we judge if it’s any good? Well, we went and explored the literature and found this 2024 paper in Nature Reviews Neurology Parkinson disease therapy: current strategies and future research priorities. It is unclear how they arrived at their list, I assume expert opinion.

But the comparison—using ChatGPT—is fascinating. Not just in terms of where they align, but also in their differences. For me, the standout difference is that Nature Reviews Neurology places a strong emphasis on interventions such as pharmaceutical drugs and surgical treatments. This seems intuitively correct, but it also reminds me of Iain Chalmers’ work on research prioritisation.

Iain has done extensive work in this area and was the driving force behind the James Lind Alliance. In his 2015 paper Patients’, clinicians’ and the research communities’ priorities for treatment research: there is an important mismatch highlights a significant divide between research agendas. The study found that patients and clinicians often prioritise non-drug treatments, such as physical or psychological therapies, while researchers tend to focus on drug treatments.

Specifically, only 18% of the treatment priorities identified by patients and clinicians were drug-related, compared to 37% in non-commercial trials and 86% in commercial trials. The authors emphasise the need for the research community to better align with the priorities of patients and healthcare professionals, advocating for increased efforts to address issues most relevant to those directly affected.

Comparison of Research Priorities: Nature Reviews Neurology vs. Trip Database Blog

I’ve compared the research priorities outlined in Nature Reviews Neurology (“Parkinson Disease Therapy: Current Strategies and Future Research Priorities”) with those in the Trip Database Blog (“Identifying Research Gaps in Parkinson’s Disease – Prioritised”). Below is a breakdown of where they align and where they diverge.

---

1. Shared Research Priorities

1.1 Early Detection & Biomarkers

Both sources emphasize the importance of developing reliable biomarkers for early diagnosis and monitoring disease progression.

• Nature Reviews Neurology focuses on integrating sensitive biomarkers (biofluid, imaging, digital) to track disease progression and support early intervention.
• The Trip Database Blog highlights the need for non-invasive biomarkers (e.g., saliva, blood, microbiota) to improve accessibility and reduce inequities in early detection.

1.2 Advancing Symptomatic Treatments

Both sources prioritize improving symptom management, particularly for motor and non-motor complications.

• Nature Reviews Neurology discusses refining deep brain stimulation (DBS), surgical, and infusion therapies to target levodopa-resistant symptoms and delay complications.
• The Trip Database Blog explores optimal exercise regimens and cognitive training interventions to improve motor function and cognitive symptoms.

1.3 Disease Modification Strategies

Both sources recognize the need for treatments that alter the disease course, rather than just alleviating symptoms.

• Nature Reviews Neurology discusses potential disease-modifying therapies, including α-synuclein-targeting treatments, neuroinflammation approaches, and genetic therapies (LRRK2, GBA mutations, GLP-1 receptor agonists like Exenatide).
• The Trip Database Blog specifically calls out GLP-1 receptor agonists (like Exenatide) as a promising intervention and highlights the need to determine which patients will benefit most from disease-modifying therapies.

---

2. Differences Between the Two Sources

2.1 Role of Artificial Intelligence (AI) (Trip Database Blog Only)

• The Trip Database Blog explicitly prioritizes the use of AI in early diagnosis to improve accuracy and reduce misdiagnosis rates.
• Nature Reviews Neurology does not specifically mention AI, though its emphasis on biomarkers could theoretically align with AI-driven diagnostics.

2.2 Personalized vs. Population-Level Approaches

• Nature Reviews Neurology emphasizes a personalized medicine approach, tailoring therapies based on genetic and biomarker profiling.
• The Trip Database Blog is more focused on population-level solutions, such as broad biomarker screening and AI integration to ensure equitable early diagnosis.

2.3 Focus on Exercise & Lifestyle (Trip Database Blog Only)

• The Trip Database Blog highlights the need to identify the most effective exercise regimens for symptom management and potential disease modification.
• Nature Reviews Neurology does not explicitly discuss exercise or lifestyle interventions, although it does acknowledge environmental and genetic risk factors in disease development.

2.4 Deep Dive into Disease Mechanisms (Nature Reviews Neurology Only)

• Nature Reviews Neurology provides a much deeper discussion of underlying disease mechanisms, including α-synuclein aggregation, genetic mutations, and neuroinflammatory pathways.
• The Trip Database Blog focuses more on practical applications (e.g., biomarker accessibility, AI tools, and exercise programs) rather than mechanistic research.

---

Conclusion

Key Overlaps:

✅ Early Detection & Biomarkers – Both emphasize the importance of finding reliable, accessible biomarkers.
✅ Symptom Management – Both focus on improving treatments for motor and non-motor symptoms.
✅ Disease Modification – Both support research into disease-modifying therapies, particularly GLP-1 receptor agonists (like Exenatide).

Key Differences:

❌ AI in Diagnosis – Only Trip Database Blog prioritizes the role of AI in improving diagnosis.
❌ Exercise & Lifestyle – Only Trip Database Blog emphasizes exercise regimens as a research priority.
❌ Personalized Medicine vs. Population-Level Approaches – Nature Reviews Neurology focuses on tailored treatments based on genetics and biomarkers, while the Trip Database Blog highlights broad interventions for all patients.
❌ Mechanistic Research Focus – Nature Reviews Neurology is heavily focused on underlying disease mechanisms, while the Trip Database Blog is more focused on applied interventions.

UPDATE: Shortly after publishing this blog, I had a thought—does any literature exist on prioritisations? It turns out there is, which inspired a new blog: Identifying Research Gaps in Parkinson’s Disease – Prioritised and Compared.

“All gaps are weighted equally so it would also be interesting to see if it could provide some sort of ranking.” Building on yesterday’s discussion on research priorities, I leveraged AI/LLMs to do the heavy lifting. I asked two separate LLMs (ChatGPT and DeepSeek) to prioritize the original list based on things such as potential impact on disease burden, equity, and feasibility of implementation. I then used the LLMs to reconcile their suggestions, leading to the following final prioritization:

Top Priority (High Impact, Feasible, and Addresses Urgent Needs & Equity)

These research areas have the highest potential to significantly alter disease progression, improve early diagnosis, and enhance patient quality of life, while being feasible for near-term implementation.

1. Lack of Reliable Biomarkers for Early Detection

• Why? Early diagnosis is critical for slowing disease progression and improving quality of life. Biomarkers (e.g., α-synuclein, mitochondrial DNA, blood-based markers) could transform Parkinson’s care by enabling earlier intervention.
• Feasibility: Advances in genetic, imaging, and blood-based biomarkers are progressing rapidly.
• Equity: Biomarkers would increase access to earlier diagnosis, especially if they can be non-invasive and cost-effective.

2. Non-Invasive Biomarkers for Disease Detection & Progression Monitoring

• Why? Saliva, blood, and microbiota-based markers offer less invasive and more accessible alternatives to expensive imaging or invasive procedures.
• Feasibility: These biomarkers are already under active investigation and could be implemented relatively quickly with further validation.
• Equity: Improves accessibility in low-resource settings, benefiting underserved populations.

3. Integration of AI for Early & Accurate Diagnosis

• Why? AI-based tools enhance diagnostic accuracy, potentially reducing misdiagnoses and allowing earlier intervention.
• Feasibility: AI-based algorithms for imaging, digital cognitive assessments, and motor function tracking are already in development and testing.
• Equity: If designed with diverse datasets, AI can expand diagnostic access to regions with limited specialists.

4. Understanding Non-Motor Symptoms as Early Indicators

• Why? Non-motor symptoms (e.g., cognitive decline, gut microbiota changes, depression, psychiatric symptoms) often appear years before motor symptoms, making them valuable for early diagnosis.
• Feasibility: Ongoing research suggests non-motor symptoms could be integrated into diagnostic criteria with further validation.
• Equity: Improves early detection and personalized treatment in diverse populations.

5. Role of GLP-1 Receptor Agonists as Disease-Modifying Treatments

• Why? GLP-1 agonists (e.g., Lixisenatide, Exenatide) have shown promise in slowing disease progression, addressing a major unmet need.
• Feasibility: These drugs are already approved for diabetes and other conditions, making repurposing feasible.
• Equity: If effective, they could be integrated into treatment regimens globally.

6. Best Exercise Type & Intensity for Motor Symptoms

• Why? Exercise is a low-cost, widely accessible intervention with proven benefits for motor symptoms and no significant side effects.
• Feasibility: Exercise is already widely studied, but optimal regimens for symptom management still need refinement.
• Equity: Can be implemented globally at low cost, benefiting all socioeconomic groups.

7. Cognitive Training Interventions for Parkinson’s-Related Dementia & Mild Cognitive Impairment

• Why? Cognitive decline significantly impacts quality of life and caregiver burden, yet current treatments are limited.
• Feasibility: Digital and behavioral cognitive training interventions are emerging and could be scaled up.
• Equity: Can be widely accessible via mobile apps, online programs, and community health initiatives.

---

Final Adjustments & Notes on Reconciliation

• Both original lists shared the same core priorities, but some terminology and emphasis differed.
• Equity considerations were harmonized—especially in how early detection, AI, and exercise could reach underserved populations.
• Feasibility analysis was refined to ensure short-to-medium-term implementation is realistic.
• The ranking maintains a balance between high-impact, scalable interventions and urgent research gaps.

Final comments: Looks and sounds plausible but we have no way of verifying the ‘accuracy’ of the above. As with yesterday, and interesting experiment none-the-less!

We host one of the largest dedicated collections of systematic reviews on the web, with over 550,000 reviews available. With this vast resource comes a responsibility: ensuring that users critically assess the validity of the systematic reviews they access.

Systematic reviews sit at the top of the evidence pyramid/hierarchy, but their inclusion in this category does not automatically guarantee high-quality evidence. While well-conducted systematic reviews deserve their status, many are poorly executed and risk misleading users.

To address this, we have developed a scoring system designed to help users gauge the reliability of systematic reviews. By identifying markers of both rigorous methodology and potential weaknesses, this system will generate a score displayed alongside each review, providing a quick and informed assessment of its credibility.

These scores may be controversial, and we recognize the limitations of any scoring system. To ensure transparency, we provide an explanation of each scoring element and how it impacts the final score. Ultimately, the primary aim of this system is to encourage healthy scepticism among our users.

Elements

Age of review?: The older the review the more likely new research has been published, meaning the systematic review out of date. Not exact as there might be no new research, but hopefully the sentiment is clear.

• 0-2 years = Good
• 3-5 = OK
• 6+ = Cause for concern

Number of Authors?: Systematic reviews require rigorous methodology, and having a single author raises concerns about bias and robustness. While more isn’t always better, a minimum of three authors is generally seen as best practice.

• 1 author = Red flag
• 2 authors = Cause for concern
• ≥ 3 = No concerns

Databases Searched ?: A systematic review should search multiple databases to minimize bias and ensure comprehensive coverage of relevant literature. A review relying on just one database is highly problematic, and even two may be insufficient.

• 1 database = Bad
• 2 = Cause for concern
• ≥ 3 = No concerns

Mention of registration?: Registering systematic reviews (e.g., in PROSPERO) is considered best practice. Reviews published before PROSPERO’s launch in 2011 won’t have this option, but they may already be affected by the Age of Review criterion.

• Mentions PROSPERO or similar registry = Good
• No mention of registration = Cause for concern

Mention of quality tools ?: Quality assessment frameworks such as GRADE, AMSTAR, and PRISMA enhance systematic review rigor. Their inclusion signals a commitment to high methodological standards.

• Mentions any of these tools = Good
• No mention = Cause for concern

Retraction watch check for the article and referenced work?: We check the Retraction Watch database for articles that are included in systematic reviews. Any retraction gets a Red Flag, regardless of the contribution to the systematic review.

• No retractions = No cause for concern
• Any retractions = Red flag

Each element is given a score and these are combined to assign one of three scores:

Some obvious issues with the score, here are a handful, I’m sure others will highlight other issues:

• Abstract-based assessment – The system evaluates what is explicitly mentioned in the abstract, not the full text.
• Text-matching accuracy – Automated detection of databases, PROSPERO registration, etc., may not be 100% accurate.
• Reliance on DOIs – Missing digital object identifiers (DOIs) can disrupt the scoring process.
• No text – Our system needs text to analyse, so no text = no score. Text might be absent if there are redirects, or other website anomalies.
• Arbitrary thresholds – The cutoffs for each category are subjective and may evolve over time.

Whitelisted sources

For a number of trusted publishers we default to ‘Appears promising’, these are typically national bodies such as IQWiG, CADTH, NICE and Cochrane. We still look for retractions (where the publication has a DOI) and will ‘red flag’ them if there is a retraction.

Summary

So, there you have the scoring system, flaws and all. Despite these imperfections, we are excited to release this scoring system. Its primary purpose is to encourage critical thinking, ensuring that users do not accept systematic reviews at face value but instead engage with them sceptically and thoughtfully.

UPDATE: After a reader’s comment we have now generated a prioritised list.

Following on from last weeks post on research gaps, I took the top 200 articles on Trip for the search Parkinson’s Disease and sent these to ChatGPT and DeepSeek asking them to generate a list of research gaps, based on the 200 articles. I then reconciled them to create a list whereby it was included if both LLMs suggested the research gaps.

I asked ChatGPT to explain the inclusion, this is the ‘conversation’:

Trip: In generating this list to what extent what is informed by the initial 200 articles?

ChatGPT: The final list of research gaps was directly informed by the 200 articles, but in a structured and synthesized manner….The research gaps were extracted based on recurring topics, unanswered clinical questions, and areas where further studies were explicitly mentioned in systematic reviews, meta-analyses, and clinical trials.

Trip: But how did you know these were research gaps?

ChatGPT: The research gaps in Parkinson’s disease were identified through:

• Reviewing the 200 articles and extracting recurring themes.
• Extracting explicitly stated research gaps from systematic reviews and meta-analyses.
• Noting controversies and inconsistencies across studies.
• Assessing where the evidence base is weak or inconclusive.
• Identifying new technologies or therapies that require validation.
• Cross-referencing with a second research gap list to confirm priority areas.
• Refining the final list to only include gaps mentioned twice, ensuring strong evidence support.

It all feels a bit like magic. Is it any good, does it work? Here’s the result:

Early Diagnosis & Biomarkers

• Lack of reliable biomarkers for early detection: Current genetic, imaging, and blood-based biomarkers (e.g., α-synuclein, mitochondrial DNA copy number, inflammatory markers) are being studied, but their clinical utility remains uncertain.
• Integration of AI for early and accurate diagnosis: Machine learning and AI-based tools (e.g., imaging analysis, digital cognitive assessments) require validation in diverse populations.
• Non-invasive biomarkers for disease detection and progression monitoring (e.g., blood, saliva, fecal microbiota): These could improve accessibility and patient compliance but need further validation.

Disease Progression & Prognostic Markers

• Understanding non-motor symptoms as early indicators: Cognitive decline, psychiatric symptoms, and gut microbiota changes often precede motor symptoms but are not well integrated into diagnostic criteria.
• Prediction of disease trajectory: Research is needed on fMRI connectivity alterations, functional MRI biomarkers, and gait analysis to predict disease progression.

Novel & Personalized Therapies

• Role of GLP-1 receptor agonists as disease-modifying treatments: Lixisenatide and Exenatide have shown promise in slowing motor disability progression, but their long-term effects need further validation.
• Targeting neuroinflammation and ferroptosis: Drugs like PTC-041 and natural compounds (e.g., Campsis grandiflora, Cissus tuberosa) show neuroprotective effects but lack large-scale clinical trials.
• Microbiome-based therapies: Fecal microbiota transplantation (FMT) is being explored for symptom management, but more research is needed on gut-brain axis mechanisms.
• Efficacy and safety of non-invasive brain stimulation (e.g., transcranial direct current stimulation, vagus nerve stimulation, MRI-guided focused ultrasound) for Parkinson’s symptoms.

Optimizing Exercise & Rehabilitation Strategies

• Best exercise type and intensity for motor symptoms: Tai Chi, aerobic exercise, resistance training, and boxing have been studied, but optimal protocols for symptom management remain unclear.
• Impact of exercise on non-motor symptoms (e.g., cognition, mood, sleep): Most research focuses on motor symptoms, while cognitive and emotional benefits of physical activity are less understood.
• Role of technology in rehabilitation: Augmented reality, gamified virtual reality environments, and sensor-based remote monitoring are promising but lack large-scale trials.

Cognitive Decline & Mental Health

• Cognitive training interventions for Parkinson’s disease-related dementia and mild cognitive impairment.
• Psychosocial impact of Parkinson’s disease: Studies on identity changes, caregiver burden, and quality of life interventions are limited.

Gender & Health Disparities in Parkinson’s Disease

• Underrepresentation of women in clinical trials: Many studies focus predominantly on male participants, potentially overlooking gender-specific disease progression and treatment responses.
• Global variations in Parkinson’s disease prevalence and treatment: Research is needed to understand geographic and socioeconomic disparities in diagnosis and care.

Advanced Disease & Palliative Care

• Management of late-stage complications: More research is needed on dysphagia, psychosis, and palliative care approaches for advanced Parkinson’s patients.
• Integration of digital monitoring for remote care: Devices for remote symptom tracking are emerging but lack validation for real-world clinical decision-making.

Gut-Brain Axis & Parkinson’s Disease

• Role of gut microbiota in disease onset and progression: Changes in the microbiome are linked to Parkinson’s, but causation remains unclear.
• Association between Parkinson’s and inflammatory bowel disease (IBD): A potential link exists, but research on therapeutic implications is lacking.

Pharmacological Treatment Optimization

• Personalized medicine approaches: Biomarker-driven treatment selection for motor and non-motor symptoms needs further development.
• Balancing effectiveness and side effects of levodopa and adjunct therapies: Strategies for reducing dyskinesia and motor fluctuations require further study.

Emerging Technologies in Diagnosis & Treatment

• Wearable and AI-driven diagnostics: Digital monitoring for motor fluctuations, gait disturbances, and cognitive decline needs validation.
• Non-invasive imaging biomarkers: Cross-modality PET image synthesis and novel neuroimaging techniques require clinical standardization.

Long-Term Outcomes and Real-World Data

• Long-term outcomes of Parkinson’s disease treatments (e.g., deep brain stimulation, levodopa, exercise) in real-world settings.

The final bit of experimentation was to explore these research gaps with those identified by the James Lind Alliance Priority Setting Partnerships. The James Lind Alliance (JLA) Priority Setting Partnerships (PSPs) bring together patients, caregivers, and healthcare professionals to identify and prioritize unanswered research questions in specific health conditions. The goal is to ensure that future research aligns with the real-world needs of those affected by the condition.

They have a list of unanswered questions for Parkinson’s Disease and although dated 2014 it was still worth asking the LLM to compare Trip’s generated list of research gaps and the James Lind List. It’s a long list so here is the conclusions of the comparison:

Key Observations

• High Alignment: Most JLA priorities align with existing research gaps, confirming that these areas remain critical and underexplored.
• Three Missing Gaps: Some JLA priorities (urinary problems, pain management, hospital medication training) do not appear in the research gaps. This suggests these may be less studied but still important for patient care.
• Overlapping Themes: Several gaps (e.g., exercise, personalized medicine, digital monitoring, cognitive training) apply to multiple JLA priorities, reinforcing their importance in Parkinson’s research.

So, there you go, a simple experiment. An interesting experiment. A worthwhile experiment? It was simple to do, it was definitely interesting, but worthwhile – I’ve no idea!

It doesn’t have to be Spanish, it could be French, German, Italian etc

In our embrace of LLMs I was wondering about their use in translation. We did this translation:

Spanish original: Tengo osteoporosis. Columna lumbar T-score -3.3. Cadera izda T-score -1.0. Se me ha producido fractura con aplastamiento y edema D11. La reumatóloga me ha mandado Denosumab. He leído efectos secundarios y no se qué hacer. Gracias

English translation: I have osteoporosis. Lumbar spine T-score -3.3. Left hip T-score -1.0. I have experienced a compression fracture with edema at T11. My rheumatologist prescribed Denosumab. I have read about the side effects and I don’t know what to do. Thank you.

Apparently this is very accurate. Could we harness this power into Trip? Allowing such roles as:

• Translate Spanish/German/Italian etc terms into their English equivalent to use within Trip
• Non-native English users being able to check their translated (English) terms
• Allowing free-text questions, in non-English, and searching Trip and getting the results page translated into Spanish etc
• Use any Q&A system we release to allow questions in Spanish/German/French etc and give the reply in the language of choice

In our recent work on automated Q&A we posted an answer to the question What is the Role of H. pylori Eradication in Asymptomatic Individuals? The final recommendation was:

“For asymptomatic individuals, H. pylori eradication should be considered on a case-by-case basis, prioritizing those with gastric cancer risk factors (e.g., family history, high-prevalence areas). Universal eradication is not supported by current guidelines in low-risk populations.“

Spanish translation: Para individuos asintomáticos, se debe considerar la erradicación de H. pylori de forma individualizada, priorizando a aquellos con factores de riesgo de cáncer gástrico (por ejemplo, antecedentes familiares, áreas de alta prevalencia). La erradicación universal no cuenta con el respaldo de las guías actuales en poblaciones de bajo riesgo.

German translation: Für asymptomatische Personen sollte die H. pylori-Eradikation fallweise in Betracht gezogen werden, wobei Personen mit Risikofaktoren für Magenkrebs (z. B. familiäre Vorbelastung, Gebiete mit hoher Prävalenz) priorisiert werden sollten. Eine universelle Eradikation wird von den aktuellen Leitlinien in Bevölkerungsgruppen mit geringem Risiko nicht unterstützt.

Japanese translation: 無症状の個人に対しては、H. pylori の除菌を症例ごとに検討し、胃がんのリスク因子（例：家族歴、高い有病率の地域）を持つ人を優先するべきです。低リスク集団においては、現在のガイドラインでは普遍的な除菌は推奨されていません。

I expect these are all good translations!

Resource is a potential issue, these translations are not free and if it proves popular it might be too heavy a burden (anyone know of any funding opportunities?). However, the benefits are huge. It would immediately open up the English-language evidence-base to people who can’t speak English. In addition it could support non-native English speakers to get the most out of Trip.

Déjame saber qué piensas. (Spanish)
Fais-moi savoir ce que tu penses. (French)
Lass mich wissen, was du denkst. (German)
Fammi sapere cosa ne pensi. (Italian)
Me diz o que você acha. (Portuguese)
Дай мне знать, что ты думаешь. (Russian)
让我知道你的想法。 (Chinese – Simplified)
讓我知道你的想法。 (Chinese – Traditional)
あなたの考えを教えてください。 (Japanese)
네 생각을 알려줘. (Korean)
دعني أعرف ما هو رأيك. (Arabic)
Laat me weten wat je denkt. (Dutch)
मुझे बताएं कि आप क्या सोचते हैं। (Hindi)
Ne düşündüğünü bana söyle. (Turkish)
Låt mig veta vad du tycker. (Swedish)
Daj mi znać, co myślisz. (Polish)
Cho tôi biết bạn nghĩ gì. (Vietnamese)
บอกให้ฉันรู้ว่าคุณคิดอย่างไร (Thai)
Πες μου τη γνώμη σου. (Greek)

We continue our work exploring LLMs and we’ve done more work based on last week’s question What is the Role of H. pylori Eradication in Asymptomatic Individuals?. We have asked the LLM to identify, from the question and answer, any research gaps. The response is below. It looks comprehensive, but is it any good?

---

1. Data on Low-Risk Populations

• Gap: Limited evidence on the role of H. pylori eradication in asymptomatic individuals from low-risk populations (e.g., North America, Western Europe), where gastric cancer incidence is lower.
• Rationale: Most studies focus on high-risk regions, leaving uncertainty about the cost-effectiveness and clinical impact of eradication in low-risk settings.

---

2. Adverse Effects and Risks of Eradication Therapy

• Gap: Insufficient discussion of the adverse effects and risks associated with eradication therapy, including:
  • Antibiotic resistance and its impact on treatment efficacy.
  • Side effects of eradication regimens (e.g., gastrointestinal discomfort, allergic reactions).
  • Potential disruption of the gut microbiome and long-term consequences.
• Rationale: A balanced assessment of risks versus benefits is crucial, especially for asymptomatic individuals who may not immediately benefit from treatment.

---

3. Impact on Non-Gastric Conditions

• Gap: Limited exploration of the systemic effects of H. pylori eradication on non-gastric conditions, such as:
  • Potential protective effects against asthma, inflammatory bowel disease, or other immune-modulated conditions.
  • Potential risks, such as increased incidence of GERD or esophageal adenocarcinoma.
• Rationale: Understanding the broader health implications of eradication is essential for informed decision-making.

---

4. Cost-Effectiveness in Diverse Healthcare Systems

• Gap: Lack of detailed cost-effectiveness analyses across different healthcare systems and economic settings, including:
  • Comparison of eradication programs with other preventive strategies (e.g., endoscopic screening).
  • Economic barriers to implementation in low- and middle-income countries.
• Rationale: Cost-effectiveness is critical for policy-making and resource allocation, especially in diverse global contexts.

---

5. Long-Term Follow-Up Data

• Gap: Insufficient long-term data on the durability of benefits and potential late adverse effects of eradication, particularly in:
  • Low-risk populations.
  • Subgroups with specific genetic or comorbid conditions.
• Rationale: Long-term studies are needed to assess the sustainability of eradication benefits and identify any delayed risks.

---

6. Optimal Screening Strategies

• Gap: Limited evidence on the most effective and cost-efficient screening protocols for asymptomatic individuals, including:
  • Tailored strategies for different demographic groups (e.g., age, genetic risk factors).
  • Integration of screening with other preventive health measures.
• Rationale: Effective screening strategies are essential for identifying individuals who would benefit most from eradication.

---

7. Role of Probiotics and Adjunct Therapies

• Gap: Lack of detailed discussion on the role of probiotics and adjunct therapies in improving eradication outcomes, including:
  • Effectiveness of specific probiotic strains or formulations.
  • Potential for adjunct therapies to reduce side effects or enhance eradication rates.
• Rationale: Adjunct therapies could optimise eradication protocols, but more evidence is needed to guide their use.

---

8. Ethical Considerations

• Gap: Absence of discussion on ethical considerations related to treating asymptomatic individuals, such as:
  • Balancing the benefits of prevention against the risks of unnecessary treatment.
  • Ensuring informed consent in population-based eradication programs.
• Rationale: Ethical considerations are critical for public health interventions, particularly when treating individuals who may never develop complications.

---

9. Impact of Vaccination (Future Directions)

• Gap: Limited exploration of vaccination as an alternative or complementary strategy to eradication therapy, including:
  • Current progress in H. pylori vaccine development.
  • Potential impact of vaccination on the need for eradication in asymptomatic individuals.
• Rationale: Vaccination could revolutionise H. pylori management, but its role remains underexplored.

---

10. Subgroup and Population-Specific Data

• Gap: Insufficient data on how subpopulations (e.g., by age, genetic factors, or comorbid conditions) respond to eradication therapy.
• Rationale: Tailored interventions based on subgroup-specific data could improve the efficacy and safety of eradication programs.

---

11. Long-Term Impact Beyond Cancer Prevention

• Gap: Limited understanding of the long-term impact of eradication on gastric physiology and overall health, including:
  • Potential benefits (e.g., reduced inflammation, improved nutrient absorption).
  • Unintended consequences (e.g., changes in gastric acid secretion, microbiome alterations).
• Rationale: A comprehensive understanding of long-term effects is necessary to weigh the risks and benefits of eradication.

---

Summary of Rationalised Knowledge Gaps

Gap	Details
Low-risk populations	Limited data on benefits in regions with low gastric cancer incidence.
Adverse effects of eradication	Risks and side effects of eradication therapy in asymptomatic individuals.
Non-gastric conditions	Impact of eradication on immune modulation and other diseases.
Cost-effectiveness	Economic analyses in diverse healthcare systems.
Long-term follow-up data	Durability of benefits and late adverse effects.
Optimal screening strategies	Effective and cost-efficient screening protocols for diverse populations.
Role of probiotics and adjuncts	Effectiveness of probiotics and adjunct therapies in eradication regimens.
Ethical considerations	Ethical implications of treating asymptomatic individuals.
Vaccination	Potential role of vaccination as an alternative strategy.
Subgroup-specific data	Response to eradication in subpopulations (e.g., age, genetics, comorbidities).
Long-term impact beyond cancer	Broader effects of eradication on gastric physiology and overall health.

---

Addressing the Gaps

To address these gaps, future research and reviews should:

1. Include studies from low-risk populations to assess the universal applicability of eradication programs.
2. Evaluate the risks and side effects of eradication therapy in asymptomatic individuals.
3. Investigate the systemic effects of H. pylori eradication on non-gastric conditions.
4. Conduct cost-effectiveness analyses in diverse economic settings.
5. Extend long-term follow-up studies to assess the durability of benefits and late adverse effects.
6. Develop and evaluate optimal screening strategies for diverse populations.
7. Explore the role of probiotics and adjunct therapies in improving eradication outcomes.
8. Discuss ethical considerations in population-based eradication programs.
9. Investigate the potential of vaccination as a preventive strategy.
10. Generate subgroup-specific data to tailor eradication interventions.
11. Study the long-term impact of eradication on gastric physiology and overall health.

By addressing these gaps, the evidence base can be strengthened, leading to more informed and balanced recommendations for H. pylori eradication in asymptomatic individuals.