Method of Detection of Breast Cancer in Low-Income Women
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common malignancy among women, and its timely diagnosis and treatment are of paramount importance, especially for vulnerable groups, such as low-income and uninsured women. Recent literature confirms that the method of breast cancer detection may be an important prognostic factor, but there are no studies that examine the method of breast cancer detection in low-income populations. We sought to analyze the determinants of method of detection (medical vs. self) in a cohort of low-income women with breast cancer receiving care through California's Breast and Cervical Cancer Treatment Program. METHODS: This is a cross-sectional survey analysis of 921 low-income women interviewed within 6 months of definitive surgical treatment. The outcome analyzed was self vs. medical detection of breast cancer. RESULTS: The mean age of the women was 53 years, with nearly 88% reporting an income of <$30,000 per year; 64% of women self-detected their breast cancer. Logistic regression analyses revealed that older women, Latinas, and women having any health insurance before diagnosis had lower odds of self-detecting their lesions. CONCLUSIONS: Patient age, ethnicity, and regular source of care were associated with method of breast cancer detection in a low-income underserved population. The rate of self-detection in our population correlates with the literature, but we need to improve efforts to increase mammography screening to ensure early detection of disease in this vulnerable group.
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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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".