Knowledge transfer to clinicians and consumers by the Cochrane Musculoskeletal Group.
Bibliographic record
Abstract
The Cochrane Musculoskeletal Group (CMSG) is one of 50 groups of the Cochrane Collaboration that prepares, maintains, and disseminates systematic reviews of treatments for musculoskeletal diseases. Once systematic reviews are completed, the next challenge is presenting the results in useful formats to be integrated into the healthcare decisions of clinicians and consumers. The CMSG recommends 3 methods to aid knowledge translation and exchange between clinicians and patients: produce clinical relevance tables, create graphical displays using face figures, and write consumer summaries and patient decision aids. Accordingly, CMSG has developed specific guidelines to help researchers and authors convert the pooled estimates of metaanalyses in the systematic reviews to user-friendly numbers. First, clinical relevance tables are developed that include absolute and relative benefits or harms and the numbers needed to treat. Next, the numbers from the clinical relevance tables are presented graphically using faces. The faces represent a group of 100 people and are shaded according to how many people out of 100 benefited or were harmed by the interventions. The user-friendly numbers are also included in short summaries and decision aids written for patients. The different levels of detail in the summaries and decision aids provide patients with tools to prepare them to discuss treatment options with their clinicians. Methods to improve the effects and usability of systematic reviews by providing results in more clinically relevant formats are essential. Both clinicians and consumers can use these products to use evidence-based information in individual and shared decision-making.
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 | 0.106 | 0.458 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.031 | 0.032 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.244 | 0.111 |
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".