The management of incidental findings of reduction mammoplasty specimens
Bibliographic record
Abstract
Reduction mammoplasty is one of the most commonly performed procedures in plastic surgery. Occasionally, there are findings reported by pathologists that are unfamiliar to the treating surgeon. The aim of the present study was to determine the types of pathologies encountered in reduction mammoplasty specimens. From this list of diagnoses, a best practice guideline for management will be organized to better assist plastic surgeons in the management of patients with incidental findings on pathology reports. A total of 441 pathology reports from patients who underwent bilateral or unilateral reduction mammoplasty in the past three years were identified. A list of 21 different pathologies was generated from the pathology reports, along with supplemental data from recent texts and articles. Occult carcinomas were encountered in two cases (0.45%) and high-risk lesions were found in three cases (0.68%) at the authors' institution. An algorithm was then constructed to organize the pathologies according to risk of malignancy and assign them to a management guideline. There are many different lesions encountered incidentally in reduction mammoplasty specimens that may or may not confer some cancer risk. It is important for plastic surgeons to know which lesions need closer follow-up to provide the best care for their patients.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".