Factors associated with door-in to door-out delays among ST-segment elevation myocardial infarction (STEMI) patients transferred for primary percutaneous coronary intervention: a population-based cohort study in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Compared to ST-segment elevation myocardial infarction (STEMI) patients who present at centres with catheterization facilities, those transferred for primary percutaneous coronary intervention (PCI) have substantially longer door-in to door-out (DIDO) times, where DIDO is defined as the time interval from arrival at a non-PCI hospital, to transfer to a PCI hospital. We aimed to identify potentially modifiable factors to improve DIDO times in Ontario, Canada and to assess the impact of DIDO times on 30-day mortality. METHODS: A population-based, retrospective cohort study of 966 STEMI patients transferred for primary PCI in Ontario in 2012 was conducted. Baseline factors were examined across timely DIDO status. Multivariate logistic regression was used to examine independent predictors of timely DIDO as well as the association between DIDO times and 30-day mortality. RESULTS: 2.63, 95% CI:1.59-4.35) were the strongest predictors of timely DIDO. Patients with timely ECG were more likely to have recommended DIDO times (33.0% vs 12.3%; P < 0.001). A significantly higher proportion of those who met the DIDO benchmark had timely FMC-to-balloon times (78.7% vs 27.4%; P < 0.001). Compared to patients with DIDO time ≤ 30 min, those with DIDO times > 90 min had significantly higher adjusted 30-day mortality rates (OR 2.82, 95% CI:1.10-7.19). CONCLUSIONS: While benchmark DIDO times were still rarely achieved in the province, we identified several potentially modifiable factors in the STEMI system that might be targeted to improve DIDO times. Our findings that patients who received a pre-hospital ECG were still being transferred to non-PCI capable centres suggest strategies addressing this gap may improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it