Surgical treatment of breast cancer among the elderly in the United States
Bibliographic record
Abstract
BACKGROUND: Breast-conserving therapy (BCT) has emerged as the preferred treatment for most women with early stage breast cancer. However, there is concern for underuse in the elderly, with previously documented low rates of BCT and large variations in practice patterns. The authors' purpose was to examine patterns and correlates of BCT for breast cancer in the elderly US population. METHODS: The primary outcome was receipt of BCT. The 2003 to 2004 Medicare inpatient, outpatient, and carrier files were used to identify incident breast cancer patients and the American Medical Association to ascertain surgeon information. The primary independent variables were US state where treatment was performed along with patient and surgeon sociodemographic information. Multivariate logistic regression was used for the analyses. RESULTS: BCT was performed in 81.8% of patients (N = 20,032). Variation in use of BCT across states was low, ranging from 74.2% in Utah to 84.0% in New Mexico. Several factors were significantly associated with low use of BCT: advanced patient age (>85 vs <70 years: odds ratio [OR], 0.50; 95% confidence interval [CI], 0.42-0.59); comorbidities (>3 vs ≤ 3: OR, 0.26; 95% CI, 0.24-0.28), and low socioeconomic status (SES) (lowest quintile vs highest quintile SES: OR, 0.60; 95% CI, 0.52-0.68). Variation in use of BCT by surgeon was low, although female surgeons aged 40 to 49 years and ≥ 60 years had significantly higher use compared with younger men. CONCLUSIONS: BCT has become the primary management among elderly breast cancer patients. Despite earlier studies to the contrary, there is now little variation in BCT use among Medicare patients.
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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 | 0.002 |
| 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) | 0.001 | 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 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".