Sex differences in chest pain and prediction of exercise-induced ischemia.
Bibliographic record
Abstract
OBJECTIVES: To examine sex differences pertaining to pain characteristics in patients presenting to the ambulatory emergency department (ED) with nontraumatic chest pain and to the prediction of exercise-induced ischemia on a follow-up electrocardiogram. METHODS: This was a prospective study of 131 women and 202 men (mean age 58 years) consulting the ED with a chief complaint of chest pain. Seventy-eight women and 116 men underwent exercise stress testing following the ED consultation. Chest pain location, extension, intensity and quality were measured. Chest pain was classified as nonspecific, or typical or atypical of angina. RESULTS: Women received fewer 'typical' angina pain diagnoses (P<0.05), rated their pain as more intense (P<0.05) and used more affective words to describe their pain (P<0.05) compared with men. Pain in the posterior shoulder and middle back areas were more frequently reported by women (P<0.05). The presence of pain in the right anterior and posterior shoulder, as well as the absence of pain in the left anterior shoulder, predicted ischemia (P<0.05) in both men and women. Only in men, pain in the retrosternal and right middle back areas, as well as a classification of pain as typical or atypical, further contributed to the prediction of ischemia. CONCLUSIONS: Sex differences exist in the experience of chest pain and in the prediction of exercise-induced ischemia from pain variables. Further research on the unique symptomatology of men and women is needed to optimize their medical management.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".