[Unstable angina: study of in-hospital and short-term prognosis].
Bibliographic record
Abstract
UNLABELLED: The aim of our study was to identify predictor factors of coronary ischemic events to stratify the risk. 367 patients, mean age 59 years (22-90), 288 men (88%), Coronary risk factors: Smoking (62%), diabetes (38%), hypertension (37%), hypercholesterolemia (18%). BRAUNWALD class III was predominant (60%). Electrocardiographic changes were present in 113 patients. Coronary angiography identified: 148 single-vessel disease, 92 double-vessel and 68 triple-vessel. In the hospital phase, 296 patients (80.5%) were stabilised. 65 had recurrent ischemia (17.5%), 6 myocardial infarction (1.5%) and 6 deaths (1.5%). After multivariate logistic regression the predictors factors of ischemic events were. Age > or = 65 years (p = 0.03), coronary artery bypass grafting (p = 0.05), left ventricular failure (p = 0.024), modified baseline electrocardiogram (p = 0.04), ST-segment depression (p = 0.05), without aspirin (p = 0.043) and heparin (p = 0.047). At 6 months, 181 patients were asymptomatic (59.1%), 101 had recurrent ischemia (33%), 14 myocardial infarction (4.6%) and 10 deaths (3.3%). After multivariate analysis, the predictor factors were: Age > or = 65 years (p = 0.026), previous unstable angina (p = 0.023), left main stenosis (p = 0.008) and without aspirin (p = 0.003). CONCLUSION: Our study identified a subgroup of high risk patients who would benefit most from either low-molecular-weight heparins and Glycoprotein IIb/IIIa receptor blockers with an early revascularisation strategy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".