Prescribing Practices of Endocrine Therapy for Ductal Carcinoma in Situ in British Columbia
Bibliographic record
Abstract
Purpose: The mainstay of treatment for ductal carcinoma in situ (DCIS) involves surgery in the form of mastectomy or lumpectomy. Inconsistency in the use of endocrine therapy (ET) for DCIS is evident worldwide. We sought to assess the variation in ET prescribing for patients with DCIS across a population-based radiotherapy (RT) program and to identify variables that predict its use. Methods: Data from a breast cancer database were obtained for women diagnosed with DCIS in British Columbia from 2009 to 2014. Associations between ET use and patient characteristics were assessed by chi-square test and multilevel multivariate logistic regression. The Kaplan–Meier method, with propensity score matching and Cox regression analysis, was used to assess the effects of ET on overall survival (OS) and relapse-free survival (RFS). Results: For the 2336 dcis patients included in the study, ET use was 13% in dcis patients overall, and 17% in patients with estrogen receptor–positive (ER+) tumours treated with breast-conserving surgery and RT. Significant variation in ET use by treatment centre was observed (range: 8–23%; p < 0.001), and prescription of et by individual oncologists varied in the range 0–40%. After controlling for confounding factors, age less than 50 years [odds ratio (OR): 1.72; p = 0.01], treatment centre, er+ status (OR: 5.33; p < 0.001), and RT use (or: 1.77; p < 0.001) were significant predictors of ET use. No difference in OS or RFS with the use of et was observed. Conclusions: In this population-based analysis, 13% of patients with dcis in British Columbia received ET, with variation by treatment centre (8–23%) and individual oncologist (0–40%). Age less than 50 years, ER+ status, and rt use were most associated with ET 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".