Factors Affecting Survival Among Women with Breast Cancer in Hawaii
Bibliographic record
Abstract
BACKGROUND: Given previous reports of ethnic differences in breast cancer survival among Hawaii's population, we investigated the role of adherence to treatment standards, treatment toxicity, preexisting chronic conditions, and obesity in the survival of 382 prospectively studied breast cancer patients representing six ethnic groups. METHODS: Participants were recruited from several hospitals in Honolulu. Information on tumor characteristics and treatment was abstracted from medical records. Based on the Physicians Data Query (PDQ®), we assessed compliance with recommended treatment guidelines. Vital status and cause of death data were obtained through linkage with the Hawaii Tumor Registry. Cox proportional hazard models were used to compute hazard ratios for predictors of survival. RESULTS: After a median follow-up time of 13.2 ± 3.7 years, 115 deaths had occurred, 43 from breast cancer and 72 from other causes. After adjustment, we observed only small differences in survival by ethnicity that were not statistically significant. In addition to advanced disease stage, obesity at diagnosis was a significant independent predictor of worse and receiving PDQ-recommended treatment of better breast cancer-specific and all-cause survival. Developing high-grade toxicity was associated with worse breast cancer survival, whereas comorbidity and older age at diagnosis were associated with higher all-cause mortality. Hormone receptor status, menopausal status, and type of health insurance were not associated with survival. CONCLUSIONS: These findings suggest that given access to healthcare, breast cancer patients experience similar survival rates. Although more information about mechanisms of action would be useful, it appears reasonable to recommend weight control to breast cancer survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".