Treatment Variation by Insurance Status for Breast Cancer Patients
Bibliographic record
Abstract
Few studies have examined the relationship of insurance status with the presentation and treatment of breast cancer. Using a state cancer registry, we compared tumor presentation and surgical treatments at presentation by insurance status (private insurance, Medicare, Medicaid, or uninsured). Student's t-test, Chi-square test, and ANOVA were used for comparison. P-values reflect a comparison to insured patients. From 1996 to 2005, there were 6876 cases of invasive breast cancer with either private (n = 3975), Medicare (n = 2592), Medicaid (n = 193), or no insurance (n = 116). The median age (years) at presentation was 55 for private, 76 for Medicare, 54 for Medicaid and 54 for uninsured. The mean and median tumor size (mm) were 18.5 and 15 for private; 20.9 and 15 for Medicare; 24.2 and 18 for Medicaid; and 29.5 and 17 for uninsured, respectively; (p < 0.001 for all). Fewer women with Medicare and Medicaid presented with node negative breast cancers: private, 73.4% node negative; Medicare, 79.5% (p < 0.001); Medicaid, 60.9% (p < 0.001); and uninsured, 58% (p = 0.005). Significantly more uninsured women had no surgical treatment of their breast cancer: 15.5% versus 4.3% for private (p < 0.001). Among women with non-metastatic T1/T2 tumors, 71.5% with private insurance underwent breast-conserving surgery (BCS), compared with 64.2% of Medicare (p < 0.001), 65% of Medicaid (p = 0.097), and 65.4% of uninsured (p = 0.234). The rate of reconstruction following mastectomy was higher for private insurance (36.6%), compared with Medicare (3.8%, p < 0.0001), Medicaid (26.1%, p = 0.31), and uninsured (5.0%, p = 0.0038). The presentation of breast cancer in women with no insurance and Medicaid is significantly worse than those with private insurance. Of concern are the lower proportions of BCS and reconstruction among patients who are uninsured or have Medicaid. Reduction of disparities in breast cancer presentation and treatment may be possible by increasing enrollment of uninsured, program-eligible women in a state-supported screening and treatment program.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".