Symptom Expression in Coronary Heart Disease and Revascularization Recommendations for Black and White Patients
Bibliographic record
Abstract
OBJECTIVES: We examined whether symptoms of coronary heart disease vary between Black and White patients with coronary heart disease, whether presenting symptoms affect physicians' revascularization recommendations, and whether the effect of symptoms upon recommendations differs in Black and White patients. METHODS: We interviewed Black and White patients in Pittsburgh in 1997 to 1999 who were undergoing elective coronary catheterization. We interviewed them regarding their symptoms, and we interviewed their cardiologist decision-makers regarding revascularization recommendations. We obtained coronary catheterization results by chart review. RESULTS: Black and White patients (N=1196; 9.7% Black) expressed similar prevalence of chest pain, angina equivalent, fatigue, and other symptoms, but Black patients had more shortness of breath (87% vs 72%, P=.001). When we considered only those patients with significant stenosis (n=737, 7.1% Black) and controlled for race, age, gender, and number of stenotic vessels, those who expressed shortness of breath were less likely to be recommended for revascularization (odds ratio=0.535; 95% confidence interval=0.375, 0.762; P<.001), but there was no significant interaction with race. CONCLUSIONS: Black patients reported shortness of breath more frequently than did White subjects. Shortness of breath was a negative predictor for revascularization for all patients with significant stenosis, but there was no difference in the recommendations by symptom by race.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".