Contralateral mastectomy in young women with breast cancer: A population-based analysis of predictive factors and clinical impact.
Bibliographic record
Abstract
82 Background: Contralateral mastectomy (CM) has recently been shown to be associated with survival benefit in women with breast cancer. The objectives of the present study were to describe factors predictive of CM in young women (≤35 years old) with invasive breast cancer and evaluate its impact on survival, in a large population based cohort. Methods: All women diagnosed with invasive breast cancer aged ≤35 from 1994 – 2003 treated with mastectomy were identified from the Ontario Cancer Registry. Patient demographics, complete tumour and treatment characteristics were abstracted from primary chart review. Cox proportional hazard regression was performed to assess factors associated with performance of CM and its effect on recurrence and overall survival, performance of CM was modeled as a time varying co-variate. The models were controlled for known predictors including age, tumor size, nodal status, ER/PR, LVI, histologic grade, systemic therapy and adjuvant radiation. Results: There were 628 women identified. Of these, 101 underwent a CM (16.1%). On multivariable analysis, factors predictive of CM were negative lymph node status (HR: 1.74, 95% CI [1.052-2.872]; p-value = 0.031) and negative estrogen receptor status (HR: 2.7, 95% CI [1.314-5.736]; p-value = 0.007). After a median follow up of 11 years, no significant survival benefit was observed in women undergoing CM compared to those who did not (HR: 0.95, 95% CI [0.61-1.46], p-value = 0.80). Conclusions: Performance of CM in young women with invasive breast cancer did not result in a significant survival benefit, compared to those without CM. Factors found to be predictive of performance of CM negative lymph node status and negative estrogen receptor status. Further studies are needed to determine if a subset of young women might benefit from CM.
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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 | 0.001 |
| 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 | 0.000 | 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".