Female-to-Male Gender Affirming Top Surgery: A Single Surgeon’s 15-Year Retrospective Review and Treatment Algorithm
Bibliographic record
Abstract
BACKGROUND: Mastectomy, referred to here as "Top Surgery," is an important surgical step for female-to-male (FTM) transgender patients. The goal is to excise breast tissue and create a masculine chest contour. Despite the rising demand for Top Surgery, debate still exists regarding how to select the most appropriate surgical technique to optimize aesthetic outcomes safely. OBJECTIVES: To determine the safety profile and aesthetic outcome of one surgeon's 15-year FTM Top Surgery experience. To provide an algorithm for FTM surgery technique selection based on this experience. METHODS: A retrospective chart review was performed on 679 FTM patients (1358 mastectomies) undergoing Top Surgery from October 2001 to July 2016. The author's Top Surgery algorithm utilizes two techniques, "Keyhole" and "Double Incision Free Nipple Graft (DIFNG)," based on breast ptosis, inferior vertical skin pinch, and skin elasticity. Demographic data, operative details, complications, and reoperations along with their reasons were collected and analyzed. RESULTS: Of the 679 patients, 15.3% underwent Keyhole and the remaining 84.7% underwent DIFNG procedure. The total complication rate was 18.1% and the total reoperation rate was 11.2% and these rates were shown to decrease over time. The two techniques differed significantly (P < 0.001) in operating time (136 vs 102 min), breast weight excised (215 vs 638 g), and complication rate (33 vs 16%). The aesthetic rating of results was 4.6/5 for Keyhole and 3.7/5 for DIFNG. CONCLUSIONS: Safe and aesthetically pleasing results were achieved using this simplified algorithm. Experience with FTM techniques can decrease complication and reoperation rates over time. LEVEL OF EVIDENCE: 3.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".