Contralateral Prophylactic Mastectomy in Young Women With Breast Cancer: A Population-Based Analysis of Predictive Factors and Clinical Impact
Bibliographic record
Abstract
Background and Objectives: Contralateral prophylactic mastectomy (cpm) has been increasingly common among women with unilateral invasive breast cancer (ibca) even though the data that support it are limited. Using a population-based cohort, the objectives of the present study were to describe factors predictive of cpm in young women (≤35 years) with ibca and to evaluate the impact of the procedure on mortality. Methods: All women diagnosed during 1994-2003 and treated with cpm were identified from the Ontario Cancer Registry. Logistic regression was used to identify patient and tumour factors associated with the use of cpm. Multivariate analyses were used to assess the effect of cpm on recurrence and mortality. Results: Of 614 women identified, 81 underwent cpm (13.2%). On multivariable analysis, factors associated with cpm included negative lymph node status, negative estrogen receptor status, and initial breast-conserving surgery with re-excision. At follow-up, breast cancer-specific mortality was similar for women who did and did not undergo cpm. Conclusions: Use of cpm in young women with ibca (compared with non-use) was not associated improved breast cancer-specific mortality. Factors found to be predictive of cpm were negative lymph node status, negative estrogen receptor status, and initial breast-conserving surgery followed by re-excision.
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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 | 0.001 |
| 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) | 0.001 | 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".