RETRACTED: Effects of botulinum toxin type A in the prevention and treatment of facial hypertrophic scars: A meta‐analysis
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
A meta-analysis was conducted to comprehensively evaluate the prophylactic and therapeutic efficacy of botulinum toxin type A (BTX-A) in the treatment of facial hypertrophic scars. Computerised searches were performed in databases, from their inception to November 2023, including Embase, Google Scholar, Cochrane Library, Wanfang, PubMed and China National Knowledge Infrastructure databases, focusing on randomised controlled trials (RCTs) that investigated the use of BTX-A for treating facial hypertrophic scars. Two researchers independently screened the literature, extracted data and conducted quality assessments. Stata 17.0 software was employed for data analysis. Seventeen RCTs were ultimately included, involving 1605 patients who underwent facial cosmetic surgery. The analysis revealed that compared with conventional treatments, BTX-A significantly reduced visual analogue scale (VAS) scores (standardized mean difference [SMD]: -3.50, 95% confidence interval [CI]: -5.16 to -1.84, p < 0.001) and Vancouver scar scale (VSS) scores (SMD: -2.86, 95% CI: -4.03 to -1.68, p < 0.001), and narrowed scar width (SMD: -1.80, 95% CI: -2.48 to -1.13, p < 0.001), while also enhancing the overall effectiveness of the treatment. This study indicates that BTX-A is an effective modality in the prophylaxis and treatment of facial hypertrophic scars, significantly alleviating scar-related pain and preventing scar widening, and is thus worthy of broader clinical application.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.064 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".