Cochrane Rehabilitation: 2019 annual report
Bibliographic record
Abstract
During its third year of existence, Cochrane Rehabilitation goals included to point out the main methodological issues in rehabilitation research, and to increase the Knowledge Translation activities. This has been performed through its committees and specific projects. In 2019, Cochrane Rehabilitation worked on five different special projects at different stages of development: 1) a collaboration with the World Health Organization to extract the best evidence for Rehabilitation (Be4rehab); 2) the development of a reporting checklist for Randomised Controlled Trials in rehabilitation (RCTRACK); 3) the definition of what is the rehabilitation for research purposes; 4) the ebook project; and 5) a prioritization exercise for Cochrane Reviews production. The Review Committee finalized the screening and "tagging" of all rehabilitation reviews in the Cochrane library; the Publication Committee increased the number of international journals with which publish Cochrane Corners; the Education Committee continued performing educational activities such as workshops in different meetings; the Methodology Committee performed the second Cochrane Rehabilitation Methodological Meeting and published many papers; the Communication Committee spread the rehabilitation evidence through different channels and translated the contents in different languages. The collaboration with several National and International Rehabilitation Scientific Societies, Universities, Hospitals, Research Centers and other organizations keeps on growing.
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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 | 0.054 | 0.098 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.035 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.106 | 0.097 |
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".