Assumptions for creating matrices of evidence to estimate overlap of primary studies in overviews of reviews: Protocol for a meta-research study
Bibliographic record
Abstract
Abstract Introduction Overlap of primary studies among systematic reviews (SRs) included in an overview is a major challenge, as it may bias results or artificially increase the precision of the synthesis. Matrices of evidence and corrected covered area (CCA) calculation are recommended methods to manage overlap, but there is little guidance on how to construct these matrices. This research aims to explore variations in the estimation of overlap using CCA matrices under different assumptions. Methods We will include overviews published in 2023. We will describe the methods used by authors to deal with overlap, and we will calculate a summary CCA (a CCA for the whole matrix of evidence) and a pairwise CCA (a CCA for each possible pair of included SRs), comparing the results under different assumptions that may modify the evidence matrix and thus the CCA. These assumptions include: publication-thread adjustments (i.e. the consideration of each set of references regarding a single primary study as a unique row in a matrix of evidence), scope adjustments (i.e. the consideration only of the SRs and primary studies providing useful data for a given outcome within an overview) and chronological structural missingness adjustments (i.e. the exclusion of primary studies published after a given SR for purposes of CCA calculation). We will assess overlap at an overview and outcome level. Discussion We propose clear definitions for the key assumptions for creating matrices of evidence. We expect to provide a guide for overview authors to better interpret their CCA estimations. Article Summary Strengths and limitations of this study This protocol explores the assumptions underlying the overlap assessment in overviews of systematic reviews, that so far have not been explicitly addressed. These assumptions include scope adjustments, publication-thread adjustments, structural missingness adjustments, and analysis at an overview or outcome level. We provide clear definitions for key overlap concepts that will guide authors for making their overlap assessments more explicit when using a matrix of evidence or corrected covered area approach. We plan to conduct exploratory analyses under different assumptions in a purposive sample of overview, hence, we will not comprehensively include all the overviews in the study period. We will conduct all the analysis calculating the corrected covered area for the whole matrices (overall approach) and for every possible pair of systematic reviews within each matrix (pairwise approach).
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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 | 0.379 | 0.647 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".