Disparities among Minority Women with Breast Cancer Living in Impoverished Areas of California
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Interaction effects of poverty and health care insurance coverage on overall survival rates of breast cancer among women of color and non-Hispanic white women were explored. METHODS: We analyzed California registry data for 2,024 women of color (black, Hispanic, Asian, Pacific Islander, American Indian, or other ethnicity) and 4,276 non-Hispanic white women (Anglo-European ancestries and no Hispanic-Latin ethnic backgrounds) diagnosed with breast cancer between the years 1996 and 2000 who were then followed until 2011. The 2000 US census categorized rates of neighborhood poverty. Health care insurance coverage was either private, Medicare, Medicaid, or none. Cox regression was used to model rates of survival. RESULTS: A 3-way interaction between ethnicity, health care insurance coverage, and poverty was observed. Women of color inadequately insured and living in poor or near-poor neighborhoods in California were the most disadvantaged. Women of color adequately insured and who lived in such neighborhoods in California were also disadvantaged. The incomes of such women of color were typically lower than the incomes of non-Hispanic white women. CONCLUSIONS: Women of color with or without insurance coverage are disadvantaged in poor and near-poor neighborhoods of California. Such women may be less able to bare the indirect, direct, or uncovered costs of health care for breast cancer treatment.
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.
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 | 0.000 | 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) | 0.001 | 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 it