Inter-regional differences and outcome in unstable angina. Analysis of the International ESSENCE trial
Bibliographic record
Abstract
AIMS: Worldwide there is a large variation in outcome (death, myocardial infarction and recurrent myocardial infarction) in patients with unstable angina or non-Q wave myocardial infarction. These variations may be explained by differences in characteristics of the presenting patients. Here we describe differences in patient presentation, treatment protocols and outcome and we investigate their relationship using data from the ESSENCE (Efficacy and Safety of Subcutaneous Enoxaparin in Non-Q-wave Coronary Events) trial. METHODS: A total of 2981 patients from six countries which enrolled more than 100 patients were included in the present analysis: United States, Canada, Argentina, France, the Netherlands and United Kingdom. Logistic regression analysis was performed to determine the effect of baseline characteristics on regional outcome. RESULTS: At day 30, the lowest triple end-point rate, irrespective of study drug treatment, was noted in the Netherlands (18.9%) and the highest in Argentina (30.5%). A model including the variables age > or = 65 years, prior angina, diabetes, prior aspirin use, ECG changes at baseline and diagnosis of non-Q wave myocardial infarction and dummy variables for Argentina and France resulted in concordance of about 60%. CONCLUSIONS: Inter-regional differences in outcome in unstable angina and non-Q wave myocardial infarction patients can reasonably well be explained by differences in patient characteristics. However, other so far unidentified variables present in Argentina and France also contributed to differences in outcome and their effect warrants further investigation.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".