Disparities in Timeliness of Care for U.S. Medicare Patients Diagnosed with Cancer
Bibliographic record
Abstract
BACKGROUND: Timeliness of care (rapid initiation of treatment after definitive diagnosis) is a key component of high-quality cancer treatment. The present study evaluated factors influencing timeliness of care for U.S. Medicare enrollees. METHODS: Data for Medicare enrollees diagnosed with breast, colorectal, lung, or prostate cancer while living in U.S. seer (Surveillance, Epidemiology and End Results) regions in 2000-2002 were analyzed. Patients were classified as experiencing delayed treatment if the interval between diagnosis and treatment was greater than the 95th percentile for each cancer site. The impacts of patient sociodemographic, clinical, and area-based factors on the likelihood of delayed treatment were analyzed using multivariate logistic regression. RESULTS: Black patients (compared with white patients) and patients initially treated with radiation therapy or chemotherapy (rather than surgery) had a greater likelihood of treatment delays across all four cancer sites. Hispanic status, dual Medicare-Medicaid status, location of initial treatment (inpatient vs. outpatient), and stage at diagnosis also affected timeliness of care for some cancer sites. Surprisingly, area-based factors reflecting availability of cancer care services were not significantly associated with timeliness of care or were associated with greater delays in areas with greater numbers of service providers. CONCLUSIONS: Multiple factors affected receipt of timely cancer care for members of the study population, all of whom had coverage of medical care services through Medicare. Because delays in treatment initiation can increase morbidity, decrease quality of life, shorten survival, and result in greater costs, prospective studies and tailored interventions are needed to address those factors among at-risk patient groups.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".