Breast Cancer After Reduction Mammoplasty: A Population-Based Analysis of Incidence, Treatment and Screening Patterns
Bibliographic record
Abstract
Background: The risk of breast cancer may be decreased in women who undergo reduction mammoplasty. The purpose of this study was to describe the incidence and treatment of breast cancer after reduction mammoplasty and to better understand the use of breast cancer screening modalities in these patients. Methods: This population-based retrospective analysis utilized the Discharge Abstract Database held by the Canadian Institute for Health Information and the National Ambulatory Care Reporting System to identify all women aged 20 years or older who underwent reduction mammoplasty in Alberta, Canada. The incidence and treatment of breast cancer were compared among patients who underwent reduction mammoplasty and age-sex-matched controls. Imaging utilization, including the use of mammography, ultrasound, and breast biopsy, was also compared. Results: Between 2003 and 2007, 8021 patients over 20 years old underwent reduction mammoplasty in Alberta. Patients were followed for an average of 12.6 years. Eighty-nine (1.1%) patients who underwent reduction mammoplasty developed breast cancer after surgery, compared to 453 (1.9%) controls (P < 0.0001). Among patients diagnosed with breast cancer, there was no difference in patient and tumor characteristics. Women who underwent reduction mammoplasty were more likely to undergo mastectomy for cancer (41.6% vs 1.5%; P < 0.0001) and were more likely to undergo mammography (66.7% vs 58.7%; P < 0.0001), ultrasound (29.2% vs 26.2%; P < 0.0001) and biopsy for benign disease (7.2% vs 6%, P < 0.0001) compared to controls. Conclusions: Despite an increased frequency of breast cancer screening, the incidence of breast cancer is lower after reduction mammoplasty compared with women who did not undergo breast reduction. After a diagnosis of breast cancer, surgical treatment patterns differ between groups, whereby mastectomy is more common after reduction mammoplasty.
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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 | 0.001 | 0.002 |
| 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".