The Incidence of Malignant and High-Risk Pathology Findings in Postreduction Mammaplasty Patients
Bibliographic record
Abstract
Introduction: Reduction mammaplasty is often performed to alleviate symptoms of macromastia or for symmetry after a lumpectomy in the contra-lateral breast. Abnormal pathology including breast cancer can be incidentally found in reduction mammaplasty specimens, but there is no consensus on risk factors or detection rates. This study aimed to elucidate the incidence of malignant and high-risk pathology findings in patients undergoing breast reduction in a Canadian context. Methods: We conducted a retrospective review of all reduction mammaplasty cases performed by 5 surgeons between January 2001 and May 2023. Patients were categorized into Group A, those undergoing bilateral reduction for macromastia symptoms, and Group B, those with a history of breast-conserving surgery seeking unilateral reduction postlumpectomy. Results: In total, 1383 breasts from 872 patients were examined: 1022 in Group A and 361 in Group B. Group B was significantly older (56.9 ± 9.3 vs 44.0 ± 13.9 years) whereas Group A had a significantly higher BMI (33.1 ± 8.4 vs 30.1 ± 5.8). High-risk and malignant pathology incidence was 1.4% overall. The sole malignancy detected was in a patient in Group A without prior breast cancer history. Multivariate analysis revealed BMI as a significant predictor for high-risk pathologies (OR 1.134, 95% CI [1.012-1.271]). Conclusions: Our findings align with previously reported incidence rates of pathological findings in mammaplasty specimens and highlight the correlation between BMI and pathology risk. These results underscore the importance of a comprehensive history and preoperative counselling about the possibility of further treatment following pathological discoveries during reduction mammaplasty.
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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 | 0.001 |
| 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".