Anabolic-androgenic Steroid Use Among Gynecomastia Patients
Bibliographic record
Abstract
BACKGROUND: Anabolic-androgenic steroids (AAS) are widely implicated in gynecomastia development. Surgery is the definitive treatment for cases persisting after cessation of AAS use. Currently, the relevance of AAS use to the surgical approach of gynecomastia has not been well explored. This study aims to compare patient characteristics, surgical outcomes, and surgical management of gynecomastia correction in AAS users versus nonusers. METHODS: A retrospective cohort study was performed with patients who underwent bilateral gynecomastia reduction surgery between January 2005 and August 2015 by a single surgeon at an academic hospital. Demographic data, AAS usage details, operative documentation, and follow-up outcomes were reviewed. RESULTS: A total of 964 cases were reviewed. Eleven percent (n = 105) of the patients had a history of AAS use. Compared with non-AAS users, AAS users were older at time of gynecomastia onset (15 years vs 13 years, P < 0.001) and surgery (28 years vs 25 years, P < 0.001). The AAS users had higher body mass index (27.3 kg/m vs 25.7 kg/m, P < 0.001) and a greater proportion of patients self-identified as bodybuilders (40.0% vs 22.4%, P = 0.002). Although no difference was found in the excised bilateral mastectomy volume (92.1 cm vs 76.4 cm, P = 0.20), The AAS users had significantly less lipoaspirate fat volume (250 mL vs 300 mL, P = 0.005). No difference was found in total complication rates. However, AAS users had significantly more revision mastectomy surgeries (3.8% vs 1.1%; P = 0.02). CONCLUSIONS: The unique breast composition of AAS users necessitates a surgical approach with meticulous intraoperative hemostasis and careful glandular excision to minimize recurrence and achieve comparable low complication rates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".