A Survey of Female-Specific Cardiovascular Protocols in Emergency Departments in Canada
Bibliographic record
Abstract
Background Cardiovascular diseases (CVD) remain the leading cause of death for women. However, systematic inequalities exist in how women experience clinical cardiovascular (CV) policies, programs, and initiatives. Methods In collaboration with the Heart and Stroke Foundation of Canada, a question regarding female-specific CV protocols in an emergency department (ED), or an inpatient or ambulatory care area of a healthcare site was sent via e-mail to 450 healthcare sites in Canada. Contacts at these sites were established through the larger initiative—the Heart Failure Resources and Services Inventory–conducted by the foundation. Results Responses were received from 282 healthcare sites, with 3 sites confirming the use of a component of a female-specific CV protocol in the ED. Three sites noted using sex-specific troponin levels in the diagnosis of acute coronary syndromes; 2 of the sites are participants in the hs- c Tn— O ptimizing the D iagnosis of Acut e M yocardial I nfarction/Injury in Women (CODE MI) trial. One site reported the integration of a female-specific CV protocol component into routine use. Conclusions We have identified an absence of female-specific CVD protocols in EDs that may be associated with the identified poorer outcomes in women impacted by CVD. Female-specific CV protocols may serve to increase equity and ensure that women with CV concerns have access to the appropriate care in a timely manner, thereby helping to mitigate some of the current adverse effects experienced by women who present to Canadian EDs with CV symptoms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".