Efficacy of Botulinum Toxin Type A for Prevention of Post-Mastectomy Scar in Transmen: A Prospective, Randomized Study
Bibliographic record
Abstract
BACKGROUND: Subcutaneous mastectomies in transmen have been gaining popularity. However, post-operative scars are an inevitable consequence. Recently, Botulinum neurotoxin A (BoNT-A) has shown positive effects in scar prevention. The objective of this study is to investigate the effectiveness of BoNT-A in scar prevention. METHODS: Fifteen patients who had undergone subcutaneous mastectomy were included. At 14 days post-surgery, either incoBoNT-A or a placebo was injected into the scar on each side. The primary outcome assessment measured the scar's severity using the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS). The secondary outcome assessment evaluated the scar's color using a standard measurement device. Outcome assessments were conducted until 6 months post-surgery. RESULTS: < 0.001). The objective assessment revealed a statistically significant decrease in redness values in the BoNT-A group compared to the placebo at 3 and 6 months. CONCLUSION: BoNT-A has demonstrated effectiveness in scar prevention by reducing the severity of postoperative scar formation and improving overall scar appearance.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".