Survival after breast cancer treatment: the impact of provider volume
Bibliographic record
Abstract
BACKGROUND: Research has not paid sufficient attention to the need for adequate case-mix adjustment in studies of the relationship between provider volume and performance. This study attempted to address this limitation by capturing and including 5-year survival outcomes and a wide range of case-mix variables in multivariate analyses of the volume-outcome relationship relating to breast cancer treatments. METHODS: All patients diagnosed with invasive primary breast cancer during 1996 (n = 809) were included. Patient, disease and treatment data were extracted from medical records; survival data were corroborated using official death registrations. A Cox proportional hazards approach was used to model relationships between patient, disease and service variables and risk of death. RESULTS: There were 262 deaths among 807 patients followed up; overall 5-year survival was 70%. Advancing age, higher levels of co-morbidity, late-stage disease, more positive nodes, and high-grade tumour were independently associated with lower survival (P < 0.05). Patients who received hormonal therapy (HR 0.50, 95% CI 0.28-0.89) and radiotherapy (HR 0.73, 95% CI 0.53-1.03) had a survival advantage. Using a cut-off point of > or =30 cases per annum, survival was lower for patients treated in low volume settings (HR 1.47, 95% CI 1.09-1.96) after adjustment for case mix. CONCLUSIONS: There was some evidence to support treatment in high volume settings although patient and disease variables were the major determinants of survival for patients with breast cancer.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".