Radiation therapy after breast-conserving surgery: When are we missing the mark?
Bibliographic record
Abstract
43 Background: Postoperative radiotherapy (RT) after breast conserving surgery (BCS) represents the standard of care for local control of breast cancer (BC). Despite wide dissemination of clinical guidelines, variations in practice persist. Our objective was to identify patient, disease, and physician characteristics that predict lack of consideration for RT after BCS. Methods: Cancer registry data and administrative claims for all BCs diagnosed in Quebec from 1998 to 2005 were collected. Receipt of a consultation for RT in women with non-metastatic BC treated with BCS was measured. Multivariate logistic regression was used to assess the association between patient, disease, and physician characteristics and having an RT consult. Results: 27,483 women were included. Mean age was 59 years, 76.5% had no comorbidities, and 27.6% had stage III BC. Overall, 90.1% of women were considered for RT within 1 year of diagnosis. Patients at age extremes were less likely to be considered as compared to women 50-69: those 30-49, 70-79 and 80+ had odds ratios (OR) of 0.82 (CI 0.73-0.93), 0.54 (CI 0.48-0.61) & 0.11 (CI 0.09-0.12), respectively. Women with any ER visit and women with a hospitalization (unrelated to BC) had 15% and 17% lower odds of having an RT consult, respectively. In patients with advanced disease, receiving a consultation for chemotherapy within 4 months of BCS increased the likelihood of also being considered for RT within 1 year (OR 1.54, CI 1.19-2.00). Increases in physician BCS volume in the year prior to patient diagnosis increased the chance of their patient receiving an RT consult by 7% for every additional 10 BCS performed. Conclusions: Patient age, use of non- BC-related health services and physician volume of BCS predicts use of RT. Guideline deviations in chemotherapy administration also predicts variation in RT use.
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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 | 0.021 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".