The role of Botulinum toxin type A for prevention and treatment of cleft lip palate (CLP) post-operative scar: a systematic review
Bibliographic record
Abstract
Background. Cleft lip palate (CLP) defects are the common orofacial defects found in newborns. The main treatment is lip and palate surgery, often resulting in a hypertrophic scar that significantly affects the patient's aesthetic appearance. Several studies showed the role of Botulinum toxin type A injections for prevention and treatment of CLP post-operative scarThis systematic review aims to review the role of Botulinum toxin type A for prevention and treatment of cleft lip palate surgery scar. Method. Online databases were searched for relevant studies from Google Scholar, PubMed and ProQuest. Data sources were searched using MeSH terms "botulinum toxin," "cleft lip palate," and "surgery scar" for all publications up to October 2022. All English papers regarding the role of Botulinum toxin type A in preventing and treating CLP post-operative scar were included. Papers not available in full text or English and not an experimental study were excluded. Result. Six studies are included in this systematic review consisting of three randomized controlled trials and three clinical studies. The experimental group received Botulinum toxin type A injections, while the control group received an injection of normal saline or other topical treatment. All of the included studies showed positive results regarding the role of Botulinum toxin type A marked with reduced scar width, good Vancouver Scar Scale (VSS), Visual Analogue Scale (VAS), patient satisfaction and no complications or side effects were found. Conclusion. The Botulinum toxin type A showed promising results for the prevention and treatment of CLP post-operative scar.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".