Sex and Gender Equity in Prehospital Electrocardiogram Acquisition
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».