Impact of STEMI Diagnosis and Catheterization Laboratory Activation Systems on Sex- and Age-Based Differences in Treatment Delay
Bibliographic record
Abstract
Background Women and the elderly with ST-elevation myocardial infarction (STEMI) experience longer treatment delays despite prehospital STEMI diagnosis and catheterization laboratory activation systems. It is not known what role specific STEMI referral systems might play in mediating this gap in care. We therefore examined sex- and age-based differences in STEMI treatment delay (TD) in different STEMI activation systems. Methods This observational comparative effectiveness study comprised 3 retrospective STEMI cohorts: a traditional hospital-based activation cohort (Cohort 1), an automated "physician-blind" prehospital activation cohort (Cohort 2), and a prehospital activation with real-time physician oversight cohort (Cohort 3). Outcomes of interest included sex and age group (< or ≥ 75 years) differences in suboptimal (> 90 minutes) first medical contact-to-device time (FMC-to-device) within each cohort, as well as independent predictors of suboptimal FMC-to-device and in-hospital mortality across cohorts. Results Five hundred-sixty STEMI activations were analyzed. In Cohort 1 (n = 179), women and those ≥ 75 were more likely to experience suboptimal FMC-to-device times (78.7% vs 36.4%, P = 0.02 and 85.0% vs 58.3%, < 0.01, respectively). Similar findings were observed in Cohort 3 (n = 109) (53.5% vs 32.9%, 56.5% vs 33.3%, respectively; P = 0.05, for both). In Cohort 2 (n = 272), however, there was no significant age-based difference (30.4% vs 21.7%, P = 0.18), and the gap was numerically lower but still significant for women (32.1% vs 20.1%, P = 0.04). When examining prehospital activation cohorts only, female sex ( P = 0.03), off-hours presentation ( P < 0.01), and physician oversight ( P < 0.01) were independent predictors of longer FMC-to-device times. Age ≥ 75 ( P < 0.01), Killip class ( P < 0.01), and female sex ( P = 0.04) were independently associated with in-hospital mortality. Conclusions Automated "physician-blind" STEMI activation was associated with a reduced TD gap in women and the elderly, suggesting possible systemic bias. Appropriately powered confirmatory studies are required, but incorporating automated diagnosis and catheterization laboratory activation may be a solution to treatment gaps in STEMI care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".