Racial, ethnic, and socioeconomic disparities in diagnosis, treatment, and survival of patients with breast cancer.
Bibliographic record
Abstract
e18551 Background: Racial disparities in breast cancer are well established. However, there is a paucity of literature assessing the interaction of patient, socioeconomic, and community factors on outcomes. The objective of this study was to determine the influence of race/ ethnicity, socioeconomic status (SES), and insurance status on disease presentation, access to care, and survival in breast cancer. Methods: A retrospective analysis was performed of Non-Hispanic Black (NHB), Non-Hispanic White (NHW), and Hispanic patients with non-metastatic breast cancer in the SEER cancer registry between 2007 and 2016. Multivariable binary logistic regression and Cox regression analyses were conducted. Results: A total of 382,975 patients were identified; 289,074 (75.5%) NHW, 45,821 (12.0%) NHB, and 48,080 (12.6%) Hispanic patients. On multivariate analysis (see table), NHB (OR 1.18, 95%CI: 1.15-1.20) and Hispanic (OR 1.20, 95%CI: 1.17-1.22) patients were more likely to present with higher stage disease than NHW patients. There was an increased likelihood of not undergoing primary resection in NHB (OR 1.56, 95%CI: 1.49-1.65) and Hispanic (OR 1.41, 95%CI: 1.34-1.48) patients compared to NHWs. Similarly, NHB and Hispanic patients had increased odds of not undergoing breast reconstruction following mastectomy (OR 1.07, 95%CI: 1.03-1.11 and OR 1.60, 95%CI 1.54-1.66, respectively). NHB patients had increased hazard for all-cause mortality (HR: 1.13, 95%CI 1.10-1.16) and breast cancer-specific mortality (HR: 1.20, 95%CI 1.16-1.24). All-cause mortality increased across SES categories (lower SES: HR 1.33, 95%CI 1.30-1.37, middle SES: HR 1.20, 95%CI 1.17-1.23) in NHBs. Conclusions: This population-based analysis confirms worse disease presentation, access to surgical therapy, and survival across racial, ethnic, and socioeconomic factors. These disparities were compounded across worsening SES, suggesting structural racism may partly account for our findings.[Table: see text]
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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 | 0.000 | 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) | 0.005 | 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".