Appraising the Evidence: Applying Sex- and Gender-Based Analysis (SGBA) to Cochrane Systematic Reviews on Cardiovascular Diseases
Bibliographic record
Abstract
OBJECTIVE: To examine the use of sex- and gender-based analysis (SGBA) in systematic reviews of cardiovascular health in order to strengthen the evidence base for clinical practice and policy. METHODS: To determine the current status of SGBA in systematic reviews, an appraisal tool was developed by the research team and applied by an independent reviewer to a random sample of 38 Cochrane systematic reviews. The sample was drawn from reviews addressing interventions for cardiovascular diseases (CVD). A random sample of Cochrane reviews in cardiovascular health was selected from the Cochrane Library, Issue 3, 2001, to Issue 3, 2007. The main outcome measure was the number of reviews that included analysis of sex or gender or both. RESULTS: Our findings showed that SGBA was generally absent in the sampled reviews. Data were rarely disaggregated by sex; only 2 of 38 reviews reported any sex or gender research gaps. Only one quarter of the reviews included a rationale as to why any subgroup analyses by sex were or were not completed. None of the 38 reviews met all of the appraisal tool criteria. As well, we found that where sex or gender was mentioned, the terms were used interchangeably. CONCLUSIONS: Despite increasing evidence over the past decade documenting that sex and gender frequently matter in CVD, this study demonstrated that SGBA was rarely considered in systematic reviews. We suggest this omission has important implications for assuring the quality of research and of evidence-based policy and practice and for achieving equitable health outcomes for women and men. To build a robust evidence base for future work in cardiovascular health, we propose that the methodologies of systematic reviews and of SGBA be refined and synchronized to enhance the collection, synthesis, and analysis of evidence for decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".