Evidence summaries: the evolution of a rapid review approach
Bibliographic record
Abstract
BACKGROUND: Rapid reviews have emerged as a streamlined approach to synthesizing evidence - typically for informing emergent decisions faced by decision makers in health care settings. Although there is growing use of rapid review 'methods', and proliferation of rapid review products, there is a dearth of published literature on rapid review methodology. This paper outlines our experience with rapidly producing, publishing and disseminating evidence summaries in the context of our Knowledge to Action (KTA) research program. METHODS: The KTA research program is a two-year project designed to develop and assess the impact of a regional knowledge infrastructure that supports evidence-informed decision making by regional managers and stakeholders. As part of this program, we have developed evidence summaries - our form of rapid review - which have come to be a flagship component of this project. Our eight-step approach for producing evidence summaries has been developed iteratively, based on evidence (where available), experience and knowledge user feedback. The aim of our evidence summary approach is to deliver quality evidence that is both timely and user-friendly. RESULTS: From November 2009 to March 2011 we have produced 11 evidence summaries on a diverse range of questions identified by our knowledge users. Topic areas have included questions of clinical effectiveness to questions on health systems and/or health services. Knowledge users have reported evidence summaries to be of high value in informing their decisions and initiatives. We continue to experiment with incorporating more of the established methods of systematic reviews, while maintaining our capacity to deliver a final product in a timely manner. CONCLUSIONS: The evolution of the KTA rapid review evidence summaries has been a positive one. We have developed an approach that appears to be addressing a need by knowledge users for timely, user-friendly, and trustworthy evidence and have transparently reported these methods here for the wider rapid review and scientific community.
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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 | 0.609 | 0.803 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.048 | 0.039 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.042 | 0.030 |
| Open science | 0.013 | 0.019 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 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".