The evolution of a new publication type: Steps and challenges of producing overviews of reviews
Bibliographic record
Abstract
BACKGROUND: To date, the Cochrane Child Health Field has published 18 overviews of reviews in our journal, Evidence-based Child Health: A Cochrane Review Journal. In this article, we highlight some of the logistical and methodological challenges of producing such syntheses. As this is a new and evolving publication type, we hope that our experience will benefit others who engage in this process. Current Methods: We discuss the process we have developed to produce overviews of reviews relevant to our mandate, including identification of the research question, establishment of the author team, selection of outcomes and included SRs, and presentation of findings. Ongoing Development: We discuss the lessons we have learned, outstanding challenges for overview authors, and the limitations of overviews. CONCLUSIONS: Overviews of reviews are only as good as the SRs and primary studies on which they are based; gaps or lack of currency in this evidence will weaken the overview of reviews. Future directions in this work must address questions of bias and loss of information. Methods for overviews of reviews targeted for specific groups, such as children, need more elaboration. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.781 | 0.917 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.060 | 0.041 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.055 | 0.046 |
| Open science | 0.013 | 0.021 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".