<b>Umbrella-Review, Evaluation, Analysis and Communication Hub (U-REACH</b>): <b>a novel living umbrella review knowledge translation approach</b>
Bibliographic record
Abstract
Systematic reviews and meta-analyses have become crucial for evidence-based decision-making in recent decades. However, it is common for the results of multiple reviews on the same topic to be inconsistent, and it is widely recognised that the results of the reviews are not always effectively communicated to healthcare professionals and the lay public. This manuscript proposes a strategy to summarise and communicate the findings of previous systematic reviews and meta-analyses to wider audiences. The proposed approach couples the findings of umbrella reviews with the creation of open-access online platforms that present the results of these umbrella reviews in an accessible way to various stakeholders. The key potential methodological avenues of this approach are presented, and specific examples from the author's own works and those from other teams are provided. An accompanying website (https://u-reach.org/) has been designed to present this Umbrella-Review, Evaluation, Analysis, and Communication Hub (U-REACH) approach and to overcome the technical challenges associated with this type of project (by sharing the code used to build existing U-REACH projects). The present document is intended to serve as a methodological and technical guide for the creation of large-scale projects designed to synthesise and disseminate scientific information to a broad audience.
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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 | 0.471 | 0.704 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.040 | 0.031 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.026 | 0.020 |
| Insufficient payload (model declined to judge) | 0.051 | 0.052 |
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".