Patient Preferences for Postoperative Drains Following Gender Affirming Mastectomy: A Modified Standard Gamble Approach
Bibliographic record
Abstract
Introduction: The necessity of postoperative drains in gender-affirming mastectomy remains unclear, with no consensus on their role in reducing complications such as seroma formation. Given this paucity of evidence, the use of drains is often at the discretion and judgment of the surgeon. Understanding patient preferences and quantifying patient risk tolerance for seroma formation and secondary procedures that may be associated with drainless mastectomy may aid in surgical decision-making and facilitate patient-centered discussions. Methods: Adolescent patients considering or having undergone gender-affirming mastectomy were surveyed. A modified standard gamble approach assessed risk tolerance for seroma formation requiring aspiration and the need for a secondary procedure. Results: Thirty participants were recruited (mean age 17.6 ± 1.3 years). Eighty percent identified as transmale, 17% as nonbinary, and 3% as gender nonconforming; 47% had a prior mastectomy. The median risk tolerance for seroma formation was 15% (interquartile range [IQR]: 5.5%–25%), and for secondary procedures, 10% (IQR: 1%–15%). Risk tolerance did not significantly differ by history of prior surgery or age. Supplementary survey findings provided insight into factors influencing concerns related to both the use of drain and drainless procedures. Conclusions: Risk tolerance for seroma and secondary procedures varies among patients, emphasizing the need for shared decision-making in gender-affirming mastectomy. Balancing patient preferences with surgical risks is essential to optimizing outcomes.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".