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
Bibliographic record
Abstract
Introduction : Systematic reviews are used to synthesise research and inform decision making by clinicians, consumers and policy makers. The synthesis component of systematic reviews is often narrowly considered as the use of statistical methods to combine the results of studies, primarily meta-analysis. However, synthesis can be considered more broadly as a process beginning with: (i) defining the groupings of populations, interventions and outcomes to be compared (the ‘PICO for each synthesis’); (ii) examining the characteristics of the available studies; and (iii) applying synthesis methods from among multiple options. To date, there has been limited examination of approaches used in reviews to define and group PICO characteristics and synthesis methods other than meta-analysis. Objectives : To identify and describe current practice in systematic reviews in relation to structuring the PICO for each synthesis and methods for synthesis when meta-analysis is not used. Methods : We will randomly sample 100 systematic reviews of the 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 studies for eligibility. One author will extract data on approaches to grouping and defining populations, interventions and outcomes, and the rationale for the chosen groups; and the presentation and synthesis methods used (e.g. tabulation, visual displays, statistical synthesis methods such as combining P values, vote counting based on direction of effect). A second author will undertake independent data extraction for a subsample of reviews. Descriptive statistics will be used to summarise the findings. Specifically, we will compare approaches to grouping in reviews that primarily use meta-analysis versus 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 methodological approaches.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.383 | 0.726 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.012 | 0.028 |
| Insufficient payload (model declined to judge) | 0.110 | 0.028 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".