Unexpected high level of severe events even in low-risk profile chest pain unit patients
Bibliographic record
Abstract
AIMS: Early heart attack awareness programs are thought to increase efficacy of chest pain units (CPU) by providing live-saving information to the community. We hypothesized that self-referral might be a feasible alternative to activation of emergency medical services (EMS) in selected chest pain patients with a specific low-risk profile. METHODS AND RESULTS: In this observational registry-based study, data from 4743 CPU patients were analyzed for differences between those with or without severe or fatal prehospital or in-unit events (out-of-hospital cardiac arrest and/or in-unit death, resuscitation or ventricular tachycardia). In order to identify a low-risk subset in which early self-referral might be recommended to reduce prehospital critical time intervals, the Global Registry of Acute Coronary Events (GRACE) score for in-hospital mortality and a specific low-risk CPU score developed from the data by multivariate regression analysis were applied and corresponding event rates were calculated. Male gender, cardiac symptoms other than chest pain, first onset of symptoms and a history of myocardial infarction, heart failure or cardioverter defibrillator implantation increased propensity for critical events. Event rates within the low-risk subsets varied from 0.5-2.8%. Those patients with preinfarction angina experienced fewer events. CONCLUSIONS: When educating patients and the general population about angina pectoris symptoms and early admission, activation of EMS remains recommended. Even in patients without any CPU-specific risk factor, self-referral bears the risk of severe or fatal pre- or in-unit events of 0.6%. However, admission should not be delayed, and self-referral might be feasible in patients with previous symptoms of preinfarction angina.
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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 | 0.003 |
| 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 | 0.000 | 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".