Rates and predictors of consideration for adjuvant radiotherapy among high-risk breast cancer patients: a cohort study
Bibliographic record
Abstract
Radiotherapy (RT) after breast conserving surgery (BCS) represents the standard for local control of breast cancer (BC). However, variations in practice persist. We aimed to characterize the rate of RT consideration (or referral) after BCS and identify predictors in Quebec, Canada, where universal health insurance is in place. A historical prospective cohort study using the provincial hospital discharge and medical services databases was conducted. All women with incident, non-metastatic BC (stages I-III) undergoing BCS (1998-2005) were identified. Odds ratios (ORs) and 95 % confidence intervals (CIs) for RT consideration were estimated with a generalized estimating equations regression model, adjusting for clustering of patients within physicians. Of the 27,483 women selected, 90 % were considered for RT and 84 % subsequently received it. Relative to women 50-69 years old, younger and older women were less likely to be considered: ORs of 0.82 (95 % CI 0.73-0.93) and 0.10 (0.09-0.12), respectively. Emergency room visits and hospitalizations unrelated to BC were associated with decreased odds of RT consideration: 0.85 (0.76-0.94) and 0.83 (0.71-0.97). Women with regional BC considered for chemotherapy were more likely to be considered for RT: 3.41 (2.83-4.11). RT consideration odds increased by 7 % (OR of 1.07, 95 % CI 1.03-1.10) for every ten additional BCSs performed by the surgeon in the prior year. Social isolation, comorbidities, and greater distance to a referral center lowered the odds. Demographic and clinical patient-related risk factors, health service use, gaps in other aspects of BC management, and surgeon's experience predicted RT consideration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".