Prehospital times in primary percutaneous coronary intervention: The new frontier for improvement
Bibliographic record
Abstract
Background: Primary percutaneous coronary intervention (PPCI) remains the treatment of choice for patients presenting with ST-elevation myocardial infarction (STEMI). With STEMI, total ischemic time is an important predictor of myocardial injury and other short and long-term adverse events including mortality. Several studies have examined ‘Door to Balloon’ times, but few studies have examined pre-hospital and in hospital component times as individual pieces that make up total ischemic time. Methods: Total ischemic and component times for patients who received PPCI from 2012- 2015 in the Queen Elizabeth-II Halifax Infirmary were described. Median total ischemic times and component times were calculated and compared. Regression modeling was performed to identify which component times and component variables explained the most variation in total ischemic times. Results: 551 patients who had successful PPCI and complete component times were identified. Most were male (76%) with a median age of 59.2 years (IQR: 52.7-68.0 years). The longest component time was ‘Symptom Onset to First Medical Contact’ (Median: 61 min, IQR: 32-138 min). ‘Symptom Onset to First Medical Contact’ was found to account for most of the variation seen in total ischemic time (R2= 61%). Conclusions: We determined that most time in the component of receiving PPCI lies in the pre-hospital setting and that component variables including EHS use and pre-activation of the cardiac catheter lab reduce total ischemic time. More research needs to be devoted to reducing patient delay, as thereappears to be little room for improvement in hospital component times.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".