Abnormal breast pathology after breast reduction surgery: A case-series and a 10-year retrospective chart review
Bibliographic record
Abstract
BACKGROUND: Incidental abnormal breast pathology identified by histological analysis following breast reduction surgery is a rare complication. This may vary from benign and premalignant pathologies to invasive breast carcinoma. Such unexpected diagnoses can pose a challenge for plastic surgeons. Few studies have examined the treatment course for this patient population. We aimed to determine whether patients with incidental breast pathology following breast reduction mammoplasty were more likely to be treated conservatively or more aggressively through prophylactic mammoplasty. METHODS: A retrospective chart review was conducted for all patients who underwent bilateral breast reduction surgery over a 10-year period. The anatomical pathology reports from all breast reduction specimens were reviewed for abnormal breast pathology findings. For patients identified with abnormal breast pathology, health records were further assessed to determine their individualized course of management. RESULTS: A total of 694 patients met the inclusion criteria. Overall, 22 patients (3.2%) showed incidental findings of abnormal breast pathology. The most common incidental pathologies were ADH (n=9, 40.9%), followed by ALH (n=6, 27.3%), and LCIS (n=5, 22.7%). There was a single case of DCIS (4.5%), and a single case of lobular microinvasive carcinoma in one breast, which was found to be ER and PR positive (4.5%). Most patients were treated conservatively, with only four of the 22 patients undergoing a prophylactic mastectomy. CONCLUSIONS: The findings of this study suggest that prophylactic mastectomy is an uncommon intervention for abnormal breast pathology following breast reduction surgery. The majority of patients with low-grade breast lesions opted for a more conservative management plan. LEVEL OF EVIDENCE: IV.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".