Primary Percutaneous Coronary Intervention during Off-Hours: One-Decade Experience from a High-Volume Cardiovascular Center
Bibliographic record
Abstract
Abstract Background The impact of performing a primary percutaneous coronary intervention (pPCI) off-hours on clinical outcomes is not well established. Objective Compare characteristics and major adverse cardiovascular events (MACE) of pPCI off-hours versus on-hours in a high-volume cardiology center. Methods Prospective cohort of patients who underwent pPCI for ST elevation myocardial infarction (STEMI) from 2009 to 2019. We defined off-hours pPCI as workdays from 8pm to 7:59 am as well as weekends and holidays. We compared patients treated on- and off-hours as to baseline characteristics and 1-year events. Results A total of 2,560 patients were treated off-hours and 1,876 patients treated on-hours. The groups were similar for most of the baseline characteristics. A higher thrombus burden was seen in patients treated off-hours (50% x 45%; p < 0.01), and in this group the radial access was more frequently used (62% x 58%; p = 0.01). Procedural success was not statistically different between the groups (95.7% x 96.4%; p = 0.21). MACE rates were higher in patients treated off-hours at 30 days (10.2% x 8.5%; p = 0.04) and at one year of follow-up (15.4% x 13.1%; p = 0.03), driven by higher death rates at 30 days (7.8% x 6.1%; p = 0.03) and at 1 year follow-up (11.1% x 9.0%; p = 0.02). Conclusion In a high-volume cardiology center, clinical characteristics, door-to-balloon times, procedural pPCI success and complication rates of STEMI patients treated on and off-hours were similar. However, patients treated off-hours presented higher MACE and mortality rates, in spite of similar MI and stroke rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".