Effect of Botulinum Toxin Application on Facial Lacerations: A Comparative Study .
Bibliographic record
Abstract
Background: Facial lacerations are frequent injuries encountered in clinical practice. Proper management is crucial to minimize scarring and preserve the aesthetic appearance. Traditional techniques for scar management include meticulous wound closure, topical treatments, and corticosteroid injections. However, recent advancements have new approaches, such as the use of botulinum toxin type A (Botox), to enhance wound healing and improve cosmetic outcomes. Aim and objectives: To evaluate the effect of Botox on improving the wound healing and minimize the scar width. Subjects and methods: This was a prospective, comparative, scar split, clinical study that was conducted on 20 patients who underwent injection of Botox on half of length of facial lacerations from January 2024 to December 2024 at Plastic Surgery Department in Minia University Hospitals. Results: the mean percentage of improvement was significantly higher among (Botox half) than (control half). Also there was significant difference regarding mean difference in total score of Vancouver scar assessment scale (VSS) after 6 months (p value <0.05). The Botox group consistently had narrower scars compared to the control group, as there was significant difference at 1,3,6 months postoperative. (p value <0.05). Conclusion: It has found that the half of facial scar which had been injected by Botox was aesthetically more acceptable and less in width than the control side.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".