Transfer of Patients With ST-Elevation Myocardial Infarction for Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
BACKGROUND: Interhospital transfer of patients with ST-elevation myocardial infarction (STEMI) for primary percutaneous coronary intervention (PPCI) is associated with longer delays to reperfusion, related in part to turnaround ("door in" to "door out," or DIDO) time at the initial hospital. As part of a systematic, province-wide evaluation of STEMI care, we examined DIDO times and associations with patient, hospital, and process-of-care factors. METHODS AND RESULTS: We performed medical chart review for STEMI patients transferred for PPCI during a 6-month period (October 1, 2008, through March 31, 2009) and linked these data to ambulance service databases. Two core laboratory cardiologists reviewed presenting ECGs to identify left bundle-branch block and, in the absence of left bundle-branch block, definite STEMI (according to both cardiologists) or an ambiguous reading. Median DIDO time was 51 minutes (25th to 75th percentile: 35-82 minutes); 14.1% of the 988 patients had a timely DIDO interval (≤30 minutes as recommended by guidelines). The data-to-decision delay was the major contributor to DIDO time. Female sex, more comorbidities, longer symptom duration, arrival by means other than ambulance, arrival at a hospital not exclusively transferring for PPCI, arrival at a center with a low STEMI volume, and an ambiguous ECG were independently associated with longer DIDO time. When turnaround was timely, 70% of patients received timely PPCI (door-to-device time ≤90 minutes) versus 14% if turnaround was not timely (P<0.0001). CONCLUSIONS: Benchmark DIDO times for STEMI patients transferred for PPCI were rarely achieved. Interventions aimed at facilitating the transfer decision, particularly in cases of ECGs that are difficult to interpret, are likely to have the best impact on reducing delay to reperfusion.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".