Graphical Representation of Overlap for <scp>OVErviews</scp>: <scp>GROOVE</scp> tool
Bibliographic record
Abstract
Overlap of primary studies among systematic reviews (SRs) is one of the main methodological challenges when conducting overviews. If not assessed properly, overlapped primary studies may mislead findings, since they may have a major influence either in qualitative analyses or in statistical weight. Moreover, overlapping SRs may represent the existence of duplicated efforts. Matrices of evidence and the calculation of the overall corrected covered area (CCA) are appropriate methods to address this issue, but they seem to be not comprehensive enough. In this article we present Graphical Representation of Overlap for OVErviews (GROOVE), an easy-to-use tool for overview authors. Starting from a matrix of evidence, GROOVE provides the number of included primary studies and SRs included in the matrix; the absolute number of overlapped and non-overlapped primary studies; and an overall CCA assessment. The tool also provides a detailed CCA assessment for each possible pair of SRs (or "nodes"), with a graphical and easy-to-read representation of these results. Additionally, it includes an advanced optional usage, incorporating structural missingness in the matrix. In this article, we show the details about how to use GROOVE, what results it achieves and how the tool obtains these results. GROOVE is intended to improve the overlap assessment by making it easier, faster, and more friendly for both authors and readers. The tool is freely available at http://doi.org/10.17605/OSF.IO/U2MS4 and https://es.cochrane.org/es/groovetool.
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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 | 0.027 | 0.229 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.393 | 0.046 |
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".