Referee report. For: The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions [version 2; peer review: 1 approved, 1 not approved]
Bibliographic record
Abstract
Introduction: Systematic reviews involve synthesis of research to inform decision making by clinicians, consumers, policy makers and researchers. While guidance for synthesis often focuses on meta-analysis, synthesis begins with specifying the ’PICO for each synthesis’ (i.e. the criteria for deciding which populations, interventions, comparators and outcomes are eligible for each analysis). Synthesis may also involve the use of statistical methods other than meta-analysis (e.g. vote counting based on the direction of effect, presenting the range of effects, combining P values) augmented by visual display, tables and text-based summaries. This study examines these two aspects of synthesis. Objectives: To identify and describe current practice in systematic reviews of health interventions in relation to: (i) approaches to grouping and definition of PICO characteristics for synthesis; and (ii) methods of summary and synthesis when meta-analysis is not used. Methods: We will randomly sample 100 systematic reviews of the quantitative effects of public health and health systems interventions published in 2018 and indexed in the Health Evidence and Health Systems Evidence databases. Two authors will independently screen citations for eligibility. Two authors will confirm eligibility based on full text, then extract data for 20% of reviews on the specification and use of PICO for synthesis, and the presentation and synthesis methods used (e.g. statistical synthesis methods, tabulation, visual displays, structured summary). The remaining reviews will be confirmed as eligible and data extracted by a single author. We will use descriptive statistics to summarise the specification of methods and their use in practice. We will compare how clearly the PICO for synthesis is specified in reviews that primarily use meta-analysis and those that do not. Conclusion: This study will provide an understanding of current practice in two important aspects of the synthesis process, enabling future research to test the feasibility and impact of different approaches.
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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 | 0.084 | 0.674 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.677 | 0.248 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".