Efficacy and safety of 1064‐nm fractional picosecond laser for the treatment of postmastectomy scars in transgender men: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: Subcutaneous mastectomy is a crucial component of gender affirmation therapy for transgender men (TM), but the scars that result from this procedure can frequently impair their quality of life. This study aimed to assess the efficacy and safety of 1064-nm fractional picosecond laser (FxPico) treatment for hypertrophic and atrophic postmastectomy scars in TM. METHODS: Twenty-two patients with a total of 35 pairs of bilateral symmetric mastectomy scars were enrolled. One of each pair of symmetric scars was randomly assigned to receive four FxPico treatments at 4-week intervals. All scars were evaluated using the modified Vancouver Scar Scale (mVSS) and three-dimensional imaging for scar roughness, melanin index, and hemoglobin index before each treatment session and at 1, 3, and 6 months following the last treatment. Additionally, participant-rated scar satisfaction (PSS) and scar improvement (Global Assessment Score, GAS), as well as adverse events were recorded. RESULTS: During the 6-month follow-up period after the end of laser treatment sessions, the treated scars showed significant reductions in the mVSS compared to the untreated controls (p < 0.001), whereas the melanin index and hemoglobin index were not significantly different. Subgroup analysis of hypertrophic scars demonstrated statistically significant reductions in mVSS at 1 (p = 0.003) and 3 months (p = 0.041) after the end of laser treatments. PSS was significantly higher on the laser-treated scars than the controls (p = 0.008), and a participant-rated GAS of 2.95 ± 0.65 was found. There were no serious adverse events reported. CONCLUSIONS: 1064-nm FxPico could be utilized to treat mastectomy scars among TM, particularly the hypertrophic type.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".