Subcutaneous Mastectomy Improves Satisfaction with Body and Psychosocial Function in Trans Men: Findings of a Cross-Sectional Study Using the BODY-Q Chest Module
Bibliographic record
Abstract
BACKGROUND: The effectiveness of gender-confirming surgery is best evaluated on the basis of patient-reported outcomes. This is the first explorative study using the BODY-Q chest module, administered in trans men before and after mastectomy. METHODS: Between October of 2016 and May of 2017, trans men were recruited to participate in a cross-sectional study. Data collection included standardized anamnesis and examination, screening questions on depression/anxiety, and seven BODY-Q scales, including new scales measuring satisfaction of the chest and nipples. Mean scores for preoperative and postoperative participants were compared, and regression analyses were conducted to identify factors associated with BODY-Q scores. RESULTS: In total, 101 persons participated (89 percent; 50 preoperatively and 51 postoperatively). Postoperative participants reported significantly higher (better) scores on the chest (67), nipple (58), body (58) (t tests, all p < 0.001), and psychological (60) (t test, p = 0.05) scales compared with preoperative patients. Postoperative chest and nipple mean scores did not differ significantly from a gynecomastia comparison, whereas scores were less favorable on the psychosocial domains. Preoperatively, chest scores were not associated with objective breast size. Lower postoperative chest scores were associated with planned revision surgery (β = -0.52) and depressive symptoms (β = -0.59). CONCLUSIONS: The present findings indicate that chest and nipple satisfaction differences in trans men undergoing mastectomy can be detected using the BODY-Q chest module. Future prospective studies are needed to measure clinical change in satisfaction and how this relates to changes in other aspects of health-related quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".