Systematic Incorporation of Sex‐Specific Information Into Clinical Practice Guidelines for the Management of ST‐Segment–Elevation Myocardial Infarction: Feasibility and Outcomes
Bibliographic record
Abstract
Background Clinical practice guideline ( CPG ) developers have yet to endorse a consistent and systematic approach for considering sex-specific cardiovascular information in CPG s. This article describes an initiative led by the Canadian Cardiovascular Society to determine the feasibility and outcomes of a structured process for considering sex in a CPG for the management of ST-segment-elevation myocardial infarction. Methods and Results A sex and gender champion was appointed to the guideline development committee. The feasibility of tailoring the CPG to sex was ascertained by recording (1) the male-female distribution of the study population, (2) the adequacy of sex-specific representation in each study using the participation/prevalence ratio, and (3) whether data were disaggregated by sex. The outcome was to determine whether recommendations for CPG s based on an assessment of the evidence should differ by sex. In total, 175 studies were included. The mean percentage of female participants reported in the studies was 24.5% ( SD : 6.6%; minimum: 0%; maximum: 51%). The mean participation/prevalence ratio was 0.62 ( SD : 0.16; minimum: 0.00; maximum: 1.19). Eighteen (10.2%) studies disaggregated the data by sex. Based on the participation/prevalence ratio and the sex-specific analyses presented, only 1 study provided adequate evidence to confidently inform the applicability of the CPG recommendations to male and female patients. Conclusions Implementing a systematic process for critically appraising sex-specific evidence for CPG s was straightforward and feasible. Inadequate enrollment and reporting by sex hindered comprehensive sex-specific assessment of the quality of evidence and strength of recommendations for a CPG on the management of ST-segment-elevation myocardial infarction.
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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 | 0.378 | 0.639 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".