Botulinum Toxin Type A for Preventing Facial Trauma and Hypertrophic Scars: A Meta‐Analysis and Trial Sequential Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness and safety of local injection of botulinum toxin type A in preventing hypertrophic scars after facial trauma and surgery using meta-analysis and sequential experimental analysis methods. METHOD: Computer retrieval of randomized controlled trials on the prevention of facial scars with botulinum toxin type A from PubMed, EMbase, the Cochrane Library, CNKI, China Biomedical Literature Service System, Wanfang Database, and VIP Chinese Science and Technology Journal Full text Database up to February 2025. RESULT: Twelve randomized controlled clinical trials were included, with a total of 644 patients; Type A botulinum toxin injection is superior to the control group in improving patient satisfaction [RR = 6.89; 95% CI (3.20, 14.85); p < 0.0001], Vancouver Scale score [RR = -1.40; 95% CI (-2.73, 0.08); p < 0.0001], visual analog score [RR = 1.41; 95% CI (0.26, 2.56); p = 0.02], scar width [RR = -0.14; 95% CI (-0.17, -0.10); p < 0.0001], and the difference is significant; The incidence of adverse events [RR = 0.38; 95% CI (0.18, 0.84); p = 0.02] and recurrence rate [RR = 0.17; 95% CI (0.04, 0.81); p < 0.03] were lower than those in the control group, and no serious adverse reactions occurred; Sensitivity analysis showed that the results were relatively robust, sequential analysis of the experiment showed that the benefits were conclusive, and Begg's and Egger's tests showed no publication bias. CONCLUSION: Type A botulinum toxin injection has a certain therapeutic effect on hypertrophic scars without significant side effects. However, the accuracy and stability of its therapeutic effect still require more high-quality research verification.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".