Investigating the correlation between hospital of primary treatment and the survival of women with breast cancer
Bibliographic record
Abstract
BACKGROUND: To understand the relation between hospital of initial treatment and the survival of women with breast cancer, the authors investigated the characteristics of the treatment center that were related most to outcome. METHODS: The authors selected women from 5 regions of Quebec, Canada, who were diagnosed with lymph node-negative breast cancer between 1988 and 1994. Data were collected by chart review, queries to physicians, and linkage with administrative data bases. Overall survival to the end of 1999 was analyzed using the Kaplan-Meier method and Cox proportional hazards models. RESULTS: The study population included 1727 women with a median follow-up of 6.8 years. The 7-year survival rate was 82% (95% confidence interval [95%CI], 80-84%). Compared with women who were treated in centers with > or = 100 new cases per year, the hazard ratio (HR) of death from any cause was 1.80 (95%CI, 1.23-2.63), 1.44 (95%CI, 1.03-2.03), and 1.30 (95%CI, 0.96-1.76) among women who were treated in hospitals with < 25 new cases, 25-49 new cases, and 50-99 new cases per year after adjusting for case mix and characteristics of the attending physician. However, the significance of caseload disappeared after adjusting for the type of hospital. By contrast, women who were treated in centers with either on-site radiotherapy, research activity, or teaching status had significantly better outcomes, even after adjusting for caseload (HR, 0.68; 95%CI, 0.50-0.92). These associations were independent of primary treatment received, which was a strong determinant of outcome. CONCLUSIONS: Primary treatment of early-stage breast cancer in larger hospitals was associated with improved survival. This relation was mediated by factors related to proficiency of care, which tended to cluster within institutions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".