Managed care enrollment and chronically disabled women with breast cancer.
Bibliographic record
Abstract
OBJECTIVE: To assess whether managed care enrollment or healthcare utilization level among women enrolled in Medicare because of disability affects stage at diagnosis and treatment of breast cancer. STUDY DESIGN: Retrospective study using the Surveillance, Epidemiology, and End Results-Medicare database. We compared breast cancer stage at diagnosis and treatment among women with disabilities enrolled in Medicare managed care versus fee-for-service (FFS) Medicare. Women enrolled in FFS Medicare were classified into levels of healthcare utilization during the 6 to 18 months before breast cancer diagnosis. METHODS: Controlling for confounders, we used regression models to determine the effects of managed care enrollment and healthcare utilization level on earlier stage at diagnosis and treatment of breast cancer. RESULTS: Disabled patients enrolled in FFS Medicare without contact with the healthcare system and those with fewer than 12 physician visits during the 6 to 18 months before breast cancer diagnosis were more likely than disabled patients enrolled in Medicare managed care to be diagnosed as having breast cancer at a late stage. There was no difference between women enrolled in Medicare managed care versus women enrolled in FFS Medicare having at least 12 physician visits during the 12-month period. Breast cancer treatment for women with disabilities did not vary across managed care enrollment or healthcare utilization level. CONCLUSION: Managed care enrollment or increased contact with healthcare providers could result in earlier stage at breast cancer diagnosis.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".