Recommendations to Improve Quality of Probiotic Systematic Reviews With Meta-Analyses
Bibliographic record
Abstract
Importance: Systematic reviews and meta-analyses often report conflicting results when assessing evidence for probiotic efficacy, partially because of the lack of understanding of the unique features of probiotic trials. As a consequence, clinical decisions on the use of probiotics have been confusing. Objective: To provide recommendations to improve the quality and consistency of systematic reviews with meta-analyses on probiotics, so evidence-based clinical decisions can be made with more clarity. Evidence Review: For this consensus statement, an updated literature review was conducted (January 1, 2020, to June 30, 2022) to supplement a previously published 2018 literature search to identify areas where probiotic systematic reviews with meta-analyses might be improved. An expert panel of 21 scientists and physicians with experience on writing and reviewing probiotic reviews and meta-analyses was convened and used a modified Delphi method to develop recommendations for future probiotic reviews. Findings: A total of 206 systematic reviews with meta-analysis components on probiotics were screened and representative examples discussed to determine areas for improvement. The expert panel initially identified 36 items that were inconsistently reported or were considered important to consider in probiotic meta-analyses. Of these, a consensus was reached for 9 recommendations to improve the quality of future probiotic meta-analyses. Conclusions and Relevance: In this study, the expert panel reached a consensus on 9 recommendations that should promote improved reporting of probiotic systematic reviews with meta-analyses and, thereby, assist in clinical decisions regarding the use of probiotics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.550 | 0.874 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.037 | 0.026 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.015 | 0.012 |
| Research integrity | 0.025 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".