Immediate Injections of Botulinum Toxin A After Surgical Excision for Ear Keloids: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Ear keloids are pathologic scar hyperplasia in the ear region. The most therapeutic approach was surgical shave excision with radiation therapy. However, radiation therapy is easily delivered to healthy surrounding tissues. In the last years, injections with botulinum toxin type A (BTX-A) have been proven to improve surgical scars effectively in clinical trials. This study aimed to evaluate the effect of immediate injections of BTX-A after surgical excision for ear keloids. METHODS: From January 2020 to January 2023, 33 consecutive patients with ear keloids were enrolled. All patients underwent scar excision and revision at the same time when they needed BTX-A. It was injected into surgical wound closure immediately after surgery. The results of this study were evaluated at follow-up from 7 to 18 months using the Vancouver Scar Scale (VSS) and the Visual Analogue Scale (VAS). RESULTS: From January 2020 to January 2023, 33 patients received concomitant therapy of immediate injections of BTX-A after surgery for ear keloids. The patients were evaluated at follow-ups lasting 7 to 18 months. Only one case recurred within the follow-up period, and no adverse effects were reported. CONCLUSION: This study demonstrates that significant cosmetic outcomes in ear keloid treatment were achieved after early postsurgical BTX-A injections. The patients reported high satisfaction and few complications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".