The Development of a Chest-Pain Protocol for Women Presenting to the Emergency Department
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death in women worldwide, and of premature death in women in Canada. Despite improvements in cardiovascular care over the past 15-20 years, acute coronary syndrome (ACS) and CVD mortality continue to increase among women in Canada. Chest pain is a common symptom leading to emergency department visits for both men and women. However, women with ACS experience worse outcomes. compared with those of men, due to misdiagnosis or lack of diagnosis resulting in delayed care and underuse of guideline-directed medical therapies. CVD mortality rates are highest in Indigenous and racialized women and those with a disproportionately high number of adverse social determinants of health. CVD remains underrecognized, underdiagnosed, undertreated, and underresearched in women. Moreover, a lack of awareness of unique symptoms, clinical presentations, and sex-and-gender specific CVD risk factors, by healthcare professionals, leads to outcome disparities. In response to this knowledge gap, in acute recognition and management of chest-pain syndromes in women, the Canadian Women's Heart Health Alliance performed a needs assessment and review of CVD risk factors and ACS pathophysiology, through a sex and gender lens, and then developed a unique chest-pain assessment protocol utilizing modified dynamic programming algorithmic methodology. The resulting algorithmic protocol is presented. The output is intended as a quick reference algorithm that could be posted in emergency departments and other acute-care settings. Next steps include protocol implementation evaluation and impact assessment on CVD outcomes in women.
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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 | 0.050 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".