How Is Health Equity Assessed in Cochrane Musculoskeletal Reviews?
Bibliographic record
Abstract
OBJECTIVE: To evaluate the extent to which Cochrane Musculoskeletal systematic reviews assess and analyze health equity considerations. METHODS: We included Cochrane Musculoskeletal systematic reviews that included trials with participants aged ≥ 50 years and that were published from 2015 to 2020. We assessed the extent to which reviews considered health equity in the description of the population in the PICO (Patient/Population - Intervention - Comparison/Comparator - Outcome) framework, data analysis (planned and conducted), description of participant characteristics, summary of findings, and applicability of results using the PROGRESS-Plus framework. The PROGRESS acronym stands for place of residence (rural or urban), race/ethnicity/culture/language, occupation, gender/sex, religion, education, socioeconomic status, and social capital, and Plus represents age, disability, relationship features, time-dependent relationships, comorbidities, and health literacy. RESULTS: In total, 52 systematic reviews met our inclusion criteria. At least 1 element of PROGRESS-Plus was considered in 90% (47/52) of the reviews regarding the description of participants and in 85% (44/52) of reviews regarding question formulation. For participant description, the most reported factors were age (47/52, 90%) and sex (45/52, 87%). In total, 8 (15%) reviews planned to analyze outcomes by sex, age, and comorbidities. Only 1 had sufficient data to carry this out. In total, 19 (37%) reviews discussed the applicability of the results to 1 or more PROGRESS-Plus factor, most frequently across sex (12/52, 23%) and age (9/52, 17%). CONCLUSION: Sex and age were the most reported PROGRESS-Plus factors in any sections of the Cochrane Musculoskeletal reviews. We suggest a template for reporting participant characteristics that authors of reviews believe may influence outcomes. This could help patients and practitioners make judgments about applicability.
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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 | 0.516 | 0.867 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.014 |
| Bibliometrics | 0.054 | 0.038 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".