Age at diagnosis predicts local recurrence in women treated with breast-conserving surgery and postoperative radiation therapy for ductal carcinoma in situ: a population-based outcomes analysis
Bibliographic record
Abstract
PURPOSE: The main goal of treating ductal carcinoma in situ (dcis) is to prevent the development of invasive breast cancer. Most women are treated with breast-conserving surgery (bcs) and radiotherapy. Age at diagnosis may be a risk factor for recurrence, leading to concerns that additional treatment may be necessary for younger women. We report a population-based study of women with dcis treated with bcs and radiotherapy and an evaluation of the effect of age on local recurrence (lr). METHODS: All women diagnosed with dcis in Ontario from 1994 to 2003 were identified. Treatments and outcomes were collected through administrative databases and validated by chart review. Women treated with bcs and radiotherapy were included. Survival analyses were performed to evaluate the effect of age on outcomes. RESULTS: We identified 5752 cases of dcis; 1607 women received bcs and radiotherapy. The median follow-up was 10.0 years. The 10-year cumulative lr rate was 27% for women younger than 45 years, 14% for women 45-50 years, and 11% for women more than 50 years of age (p < 0.0001). The 10-year cumulative invasive lr rate was 22% for women younger than 45 years, 10% for women 45-50 years, and 7% for women more than 50 years of age (p < 0.0001). On multivariate analyses, young age (<45 years) was significantly associated with lr and invasive lr [hazard ratio (hr) for lr: 2.6; 95% confidence interval (ci): 1.9 to 3.7; p < 0.0001; hr for invasive lr: 3.0; 95% ci: 2.0 to 4.4; p < 0.0001]. An age of 45-50 years was also significantly associated with invasive lr (hr: 1.6; 95% ci: 1.0 to 2.4; p = 0.04). CONCLUSIONS: Age at diagnosis is a strong predictor of lr in women with dcis after treatment with bcs and radiotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".