Does Breast Reconstruction after Mastectomy for Breast Cancer Affect Overall Survival? Long-Term Follow-Up of a Retrospective Population-Based Cohort
Bibliographic record
Abstract
BACKGROUND: This study compared overall and breast cancer-specific survival using long-term follow-up data among women diagnosed with invasive breast cancer undergoing mastectomy or breast reconstruction. METHODS: Retrospective study using population-based data from Ontario Cancer Registry (1980 to 1990) including women receiving breast reconstruction within 5 years after mastectomy and controls of age- and cancer histology-matched women with mastectomy alone. We compared overall and breast cancer-specific survival using an extended Cox hazards model. Secondary analysis examined conditional survival across early, intermediate, and late follow-up. RESULTS: Seven hundred fifty-eight matched pairs formed the cohort, with a median follow-up of 23.4 years (interquartile range, 1.1 to 33.0 years). Fewer breast reconstruction patients died overall or from breast cancer compared with controls (overall survival, 44.5 percent versus 56.7 percent, p < 0.0001; breast cancer-specific survival, 31.8 percent versus 42.6 percent, p = 0.0002, respectively). Breast reconstruction was associated with a 17 percent reduced risk of death and a 19 percent reduced risk of breast cancer death, after adjustment (overall survival hazard ratio, 0.83; 95 percent CI, 0.72 to 0.96; breast cancer-specific survival hazard ratio, 0.81; 95 percent CI, 0.68 to 0.99). Among 885 women (58 percent) surviving 20 or more years, there was no difference in risk of death from breast cancer (hazard ratio, 0.59; 95 percent CI, 0.31 to 1.10). CONCLUSION: In a large cohort with invasive breast cancer followed over 20 years, there is no evidence that breast reconstruction is associated with worse survival outcomes compared with mastectomy alone. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".