Prehospital STEMI Referral Systems and Sex-Related Bias in Canada: A National Survey
Bibliographic record
Abstract
Background: Prehospital electrocardiographic ST-elevation myocardial infarction (STEMI) diagnosis and prehospital cardiac catheterization laboratory activation have been shown to significantly reduce average treatment delay, and further standardization of such systems may help reduce sex-related treatment and outcome gaps. However, what types of prehospital STEMI activation systems are in place across Canada, and to what extent sex-based STEMI treatment disparities are tracked, is unknown. Methods: We conducted a national survey of catheterization laboratory directors between October 11 and December 25, 2021. Seventeen catheterization laboratory directors representing 6 community and 11 academic centres completed the survey (40% response rate). Results: : All responding centres use a prehospital STEMI diagnosis and cardiac catheterization laboratory activation system, and the majority (59%) rely on real-time physician oversight. Slightly less than half (47%) of percutaneous coronary intervention centres reported prospectively tracking sex-related differences in STEMI care, and only one respondent believed that a significant systemic sex-related bias was present in their prehospital STEMI referral system. Patient factors (symptom description or time to presentation; 23.5%) and limitations of electrocardiogram diagnosis of STEMI in women (23.5%) were cited most frequently as contributing to sex-related bias in STEMI referral systems. In contrast, implicit bias in the referral algorithm, prehospital provider bias, and physician bias were not considered important contributing factors. Conclusions: Although all responding centres employ prehospital activation systems, less than half tracked sex-related differences, and most respondents believed that no sex-related bias existed in their prehospital STEMI system.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".