The safety and feasibility of immediately returning patients transferred for primary percutaneous coronary intervention with ST-elevation myocardial infarction
Bibliographic record
Abstract
AIMS: To describe the safety of immediate retransfer to community hospitals following primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI). METHODS AND RESULTS: In a cohort of 246 consecutive patients transferred to a tertiary institution who all underwent primary or rescue PCI, 166 (67%) were immediately retransferred back. The retransfer occurred only if they were haemodynamically stable and had undergone an uncomplicated procedure. In-hospital adverse events were assessed in each referral hospital. Patients had a mean age of 59 years, presented an anterior MI in 39%, and 91% were in Killip class 1. In this cohort, 75% of patients underwent primary PCI and 25% received rescue PCI. A transradial approach was used in 74% of patients. During ambulance transport back to the referral hospital, no adverse events occurred. In-hospital outcomes were favourable, with low death (2.4%), reinfarction (3.6%) and stroke (1.2%) rates. TIMI major bleeding occurred in 1.8% (catheter-related in 0.6%). CONCLUSIONS: In this carefully selected population of STEMI patients, immediate retransfer to the referral hospital following primary or rescue PCI is feasible in more than 2/3 of patients and associated with a low risk of major clinical adverse events.
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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 | 0.017 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".