The effectiveness of botulinum toxin injection for reducing hypertrophic scar after cleft lip surgery
Bibliographic record
Abstract
Introduction: The most frequent complication after cleft lip surgery is HTS. This condition leads to lip asymmetry as the scar contracts, leading to a shortened lip and nasal deformity on the affected side, impaired speech development, and psychological stress. Botulinum toxin injection is known as HTS treatment. Therefore, this systematic review aims to postoperatively establish the physical outcome of cleft lip scar in patients injected with botulinum toxin. Method : The systematic review was conducted based on the PRISMA guidelines and PICO criteria. The experimental group was patients given botulinum toxin injections, and the control group was a placebo group. The results were based on the physical effects. This study was assessed with Cochrane risk-of-bias assessment tools. Five studies met the criteria. Results: Physical outcomes were significantly improved using the Vancouver Scar Scale criteria, while two studies explained no significant difference in the scar scale. The consistent results were from the scar width measurement. The experimental group's scar was narrower than that of the control group. Two studies added a visual analog scale with better results in the experimental groups. Compared with standard saline injection, botulinum toxin injection has a better outcome. This result was aligned with the results found in several previous studies. Conclusions: Botulinum toxin can promote wound healing pre or postoperatively and has narrower effects than in control groups. This study showed that overall physical outcome improved in botulinum injection for the cleft lip’s surgical scars. However, there is still a need for broader-scale studies on this topic for more reliable results.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".