Cochrane Rehabilitation: 2018 annual report
Bibliographic record
Abstract
During its second year of existence, Cochrane Rehabilitation worked hard to accomplish new and old goals. The Review Committee completed the massive task of identifying and "tagging" all rehabilitation reviews in the Cochrane library. The Publication Committee signed agreements with several international journals and started the publication of Cochrane Corners. The Education Committee performed educational activities such as workshops in International Meetings. The Methodology Committee has completed a two days Cochrane Rehabilitation Methodological Meeting in Paris of which the results will soon be published. The Communication Committee reaches almost 5,000 rehabilitation professionals through social media, and is working on the translation of contents in Italian, Spanish, French, Dutch, Croatian and Japanese. Memoranda of Understanding have been signed with several National and International Rehabilitation Scientific Societies, Universities, Hospitals, Research Centres and other organizations. The be4rehab (best evidence for rehabilitation) project has been started with the World Health Organisation (WHO) to extract from Cochrane reviews and clinical guidelines the best currently available evidence to produce the WHO Minimum Package of Rehabilitation Interventions. The Cochrane Rehabilitation ebook is under development as well as a priority setting exercise with 39 countries from all continents.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.041 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.155 | 0.117 |
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".