Sex and Gender Equity in Prehospital Electrocardiogram Acquisition
Bibliographic record
Abstract
INTRODUCTION: Research in cardiac care has identified significant gender-based differences across many outcomes. Women with heart disease are less likely both to be diagnosed and to receive standard care. Gender-based disparities in the prehospital setting are under-researched, but they were found to exist within rates of 12-lead electrocardiogram (ECG) acquisition within one urban Emergency Medical Services (EMS) agency. STUDY OBJECTIVE: This study evaluates the quality improvement (QI) initiative that was implemented in that agency to raise overall rates of 12-lead ECG acquisition and reduce the gap in acquisition rates between men and women. METHODS: This QI project included two interventions: revised indications for 12-lead acquisition, and training that highlighted sex- and gender-based differences relevant to patient care. To evaluate this project, a retrospective database review identified all patient contacts that potentially involved cardiac assessment over 18 months. The primary outcome was the rate of 12-lead acquisition among patients with qualifying complaints. This was assessed by mean rates of acquisition in before and after periods, as well as segmented regression in an interrupted time series. Secondary outcomes included differences in rates of 12-lead acquisition, both overall and in individual complaint categories, each compared between men/women and before/after the interventions. RESULTS: Among patients with qualifying complaints, the mean rate of 12-lead acquisition in the lead-in period was 22.5% (95% CI, 21.8% - 23.2%) with no discernible trend. The protocol change and training were each associated with a significant absolute level increase in the acquisition rate: 2.09% (95% CI, 0.21% - 4.0%; P = .03) and 3.2% (95% CI, 1.18% - 5.22%; P = .003), respectively. When compared by gender and time period, women received fewer 12-leads than men overall, and more 12-leads were acquired after the interventions than before. There were also significant interactions between gender and period, both overall (2.8%; 95% CI, 1.9% - 3.6%; P < .0001) and in all complaint categories except falls and heart problems. CONCLUSION: This QI project resulted in an increase in 12-leads acquired. Pre-existing gaps in rates of acquisition between men and women were reduced but did not disappear. On-going research is examining the reasons behind these differences from the perspective of prehospital providers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".