Factors Affecting Radiotherapy Prescribing Patterns in the Post-Mastectomy Setting
Bibliographic record
Abstract
Background: Radiation therapy (RT) after mastectomy for breast cancer can improve survival outcomes, but has been associated with inferior cosmesis after breast reconstruction. In the literature, RT dose and fractionation schedules are inconsistently reported. We sought to determine the pattern of RT prescribing practices in a provincial RT program for patients treated with mastectomy and reconstruction. Methods: Women diagnosed with stages 0–III breast cancer between January 2012 and December 2013 and treated with curative-intent rt were identified from a clinicopathology database. Patient demographic, tumour, and treatment information were extracted. Of the identified patients, those undergoing mastectomy were the focus of the present analysis. Results: Of 4016 patients identified, 1143 (28%) underwent mastectomy. The patients treated with mastectomy had a median age of 57 years, and 37% of them underwent reconstruction. Treatment with more than 16 fractions of rt was associated with autologous reconstruction [odds ratio (OR): 37.2; 95% confidence interval (CI): 11.2 to 123.7; p < 0.001], implant reconstruction (OR: 93.3; 95% CI: 45.3 to 192.2; p < 0.001), and treating centre. Hypofractionated treatment was associated with older age (OR: 0.94; 95% CI: 0.92 to 0.96; p < 0.001), and living more than 400 km from a treatment centre (OR: 0.37; 95% CI: 0.16 to 0.86; p = 0.02). Conclusions: Prescribing practices in breast cancer patients undergoing mastectomy are influenced by reconstruction intent, age, nodal status, and distance from the treatment centre. Those factors should be considered when making treatment decisions.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".