Hypertension is an independent predictor of survival disparity between African‐American and white breast cancer patients
Bibliographic record
Abstract
The objective of this study was to determine whether comorbidity, or pre-existing conditions, can account for some of the disparity in survival between African-American and white breast cancer patients. A historical cohort study was conducted of 416 African-American and 838 white women diagnosed with breast cancer between 1973 and 1986, and followed through 1999 in the Kaiser Permanente Northern California Medical Care Program. Information on comorbidity, tumor characteristics and breast cancer treatment was obtained from medical records, and Surveillance, Epidemiology and End Results, Northern California Cancer Center Registry. Associations between comorbidity and survival were analyzed with multiple Cox proportional hazards regression. Over a mean follow-up of 9 years, African Americans had higher overall crude mortality than whites: 165 (39.7%) versus 279 (33.3%), respectively. When age, race, tumor characteristics and breast cancer treatment were controlled, the presence of hypertension was associated with all cause survival [hazard ratio (HR) = 1.33, 95% confidence intervals (CI) 1.07-1.67] and it accounted for 30% of racial disparity in this outcome. Hypertension-augmented Charlson Comorbidity Index was a significant predictor of survival from all causes (HR = 1.32, 95%CI 1.18-1.49), competing causes (HR = 1.52, 95%CI 1.32-1.76) and breast cancer specific causes (HR = 1.18, 95%CI 1.03-1.35). In conclusion, hypertension has prognostic significance in relation to survival disparity between African-American and white breast cancer patients. If our findings are replicated in contemporary cohorts, it may be necessary to include hypertension in the Charlson Comorbidity Index and other comorbidity measures.
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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 | 0.002 |
| 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) | 0.001 | 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".