Is Botox or Dermabond Superior in the Appearance of the Lip Scar After Primary Cleft Lip Repair and Revision? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Cleft lip scar formation is an inevitable consequence of cleft lip repair (CLR) and is exacerbated by the dynamic movement of the middle face. Various methods to correct or prevent these deformities have been described including silicone sheeting, surgical revisions, laser therapy, and more recently, Botulinum toxin-A (Botox) and Dermabond. This study aims to analyze and compare the impact of Botox versus Dermabond on scar appearance after CLR. Methods: Following PRISMA guidelines, a systematic review was performed on Medline, Embase, Cochrane, and CINAHL using the following keywords: “Dermabond,” “botulinum toxin,” and “cleft lip.” Outcomes of interest were the rates of scar hypertrophy, scar width, Vancouver scar scale (VSS), visual analog scale (VAS), Hollander wound evaluation scale (HWES), and complications. Results: Nine studies were included of which 4 articles analyzed Botox and 5 analyzed Dermabond. Forest plots for scar width at the first and second time point supported the use of Botox to achieve a smaller scar width with P < .0001 (95% CI: −1.09 [−1.56 to −0.63] and 95% CI: −0.94 [−1.37 to −0.50], respectively). A significant increase in VAS was observed with Botox (95% CI: 1.66 [1.27-2.05], P value < .0001) and VSS was insignificant. Of the articles that analyzed Dermabond, scar appearance was comparable to the traditional suture closure group. There were no feeding complications for either intervention. Pooled forest plots for VAS comparing Botox and Dermabond supported the use of Botox with improved VAS (95% CI: 1.66 [1.27-2.05], P < .0001) compared to Dermabond (95% CI: 0.07 [ −0.48 to 0.61], P = .80). Conclusions: The current literature supports the use of Botox for scar improvement following CLR or revision. However, there is limited data to support Dermabond’s utility in improving scars in CLR, which highlights the need for further studies.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".