Cochrane Clinical Answers: Putting Cochrane Reviews in Clinical Context
Bibliographic record
Abstract
One of the goals of The Cochrane Collaboration's Strategy to 2020 is to make Cochrane evidence accessible and to put the needs of users at the heart of content design and delivery.[1] The Cochrane derivatives programme is a key element of this knowledge translation initiative, aimed at making Cochrane evidence available via a range of content platforms so it resonates with and meets the needs of different audiences.Cochrane Clinical Answers (CCAs; cochraneclinicalanswers.com) are evidence-based answers to clinical questions based on Cochrane Reviews and have been created to support health professionals in decision making (Figure 1).They are not simply summaries of reviews; value is added by starting with a clinical question, mimicking the way doctors approach information at the point of care, and curating and filtering the data so that the most clinically relevant aspects of the review are brought to the forefront.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.250 | 0.658 |
| Meta-epidemiology (narrow) | 0.005 | 0.011 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.087 | 0.088 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.044 | 0.055 |
| Open science | 0.014 | 0.035 |
| Research integrity | 0.036 | 0.029 |
| Insufficient payload (model declined to judge) | 0.142 | 0.085 |
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".