Defining Low Risk for Coronary Heart Disease among Women with Chest Pain Syndrome: A Prospective Evaluation
Bibliographic record
Abstract
BACKGROUND: A better understanding of the clinical manifestations of coronary disease in women may lead to earlier recognition and better outcomes. METHODS: One hundred fifty-eight women coming to primary care physicians, emergency rooms, or cardiology clinics with undefined chest pain and at least two risk factors underwent detailed clinical evaluation of risk factor profile and symptom characteristics as well as stress testing. The significance of the presenting symptoms was evaluated on the basis of clinical events during an average 26.2 months of follow-up. Noncardiac pain was diagnosed on the basis of spontaneous resolution of symptoms, establishment of an alternative diagnosis, or negative coronary angiography. Cardiac chest pain was established by the development of cardiac clinical events or angiographic demonstration of coronary disease. RESULTS: Noncardiac chest pain was established in 128 (81%) patients. The remaining 30 (19%) either were found to have had cardiac chest pain or remain symptomatic without definitive diagnosis. Multivariate analysis revealed that noncardiac chest pain was best predicted by a combination of nondiabetic status and negative stress testing. The clinical characteristics of the chest pain syndrome were not significant contributors. CONCLUSIONS: In nondiabetic women with chest pain syndrome and at least two other cardiac risk factors, a negative stress test predicts a benign course in over 2 years of follow-up.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".