Techniques for Preserving the Nipple Areolar Complex in Chest Masculinization for All Chest Sizes
Bibliographic record
Abstract
Current literature on gender-affirming top surgery techniques predominantly focuses on achieving optimal binary male aesthetic outcomes. However, goals for surgery are unique, and preservation of the nipple-areolar complex (NAC) for aesthetics and sensation can be of primary importance to patients. This paper provides an algorithm for preserving the NAC based on chest size and native NAC position. Written consent was obtained for all before and after patient photos. Photos were taken as part of patient care and documentation from one surgeon at Women's College Hospital in Toronto, Canada, between January 2020 and March 2022. Four techniques are highlighted in detail in this report: (1) keyhole (subcutaneous) mastectomies, (2) periareolar mastectomies, (3) nipple-preserving double-incision mastectomies, and (4) inverted T mastectomies. Current literature for top surgery focuses primarily on double-incision mastectomies with free nipple grafts or a smaller subset of periareolar and keyhole mastectomies. We have outlined several techniques to preserve the NAC, and an algorithm has been recommended based on chest size and preoperative NAC position. NAC preservation is possible for most chest sizes when performing masculinizing chest surgeries. The algorithm we have described provides guidance for surgeons to choose which technique to use based on the patient's breast volume, ptosis, and NAC position to preserve the entire NAC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.003 | 0.001 |
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".