The Impact of Botulinum Toxin on Scar Formation Following Thyroidectomy: A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVES: Thyroidectomy scars may lead to cosmetic issues as well as negative psychosocial effects in patients. . Botulinum toxin (BTA) is increasingly being used for facial scar reduction, but studies have also demonstrated its efficacy in treating thyroidectomy scars. The objective of this study is to systematically review studies in the literature on BTA for thyroidectomy scar formation. METHODS: Five databases (PubMed, EMBASE, MedLine, Cochrane, and Web of Science) were searched in January 2025. Inclusion criteria were English-language randomized controlled trials that used scar assessment scales (Stony Brook Scar Evaluation Scale, Modified Stony Brook Scar Evaluation Scale, Vancouver Scar Scale) or patient subjective appearance. Exclusion criteria consisted of studies on using BTA for non-thyroidectomy scars and studies lacking follow-up data and outcomes on scar appearance. The search terms used were "thyroid*," "thyroidectomy," "parathyroid," "parathyroidectomy," AND "scar," "scar management," "wound healing," "aesthetics," "scar treatment." Statistical analysis was performed in RStudio. RESULTS: Six hundred forty-five articles were initially retrieved. Thirty-one articles were reviewed for full-text review. Four randomized controlled trials were finally included for a total of 125 patients. The age range of patients was 45 to 54 years old, and most patients were female (range: 73.3%-93.3%). Improvements in scar appearance were noted across various scar measurement scales such as the Stony Brook and Vancouver Scar Scales, and in patient satisfaction. CONCLUSIONS: Botulinum injections into the thyroidectomy incision may improve aesthetic scar outcomes based on several scar analysis scales. To the authors' knowledge, they provide the first review on the use of botulinum toxin on scar formation after thyroidectomy.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".