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Record W4221084517 · doi:10.1159/000522396

The Efficacy and Safety of Botulinum Toxin Type A Injections in Improving Facial Scars: A Systematic Review and Meta-Analysis

2022· review· en· W4221084517 on OpenAlexaboutno aff
Wendi Wang, Guangjing Liu, Xiaobing Li

Bibliographic record

VenuePharmacology · 2022
Typereview
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsVisual analogue scaleRandomized controlled trialAdverse effectClinical trialMeta-analysisBotulinum toxinAnesthesiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Botulinum toxin type A (BTA) has a wide range of clinical applications, and its use in improving aesthetics is one of them. The aim of this study was to better assess the efficacy and safety of BTA in patients with facial scars. SUMMARY: We extracted the data of the visual analog scale (VAS) score, Vancouver scar scale (VSS) score, scar width, observer scar assessment scale (OSAS), patient scar assessment scale (PSAS), and/or drug-related adverse events. Five studies provided the data of VAS score, and the results showed that the VAS score in the BTA group was significantly higher than that in the control group. Three randomized controlled trials (RCTs) reported the VSS score. A statistically significant difference exists between the BTA group and the control group. Three RCTs reported the scar width after BTA treatment. A more favorable change was found in the BTA group with scar width even without statistical significance. Data about the OSAS and PSAS scores were available in two trials. There was no significant difference in OSAS and PSAS scores between the BTA group and the control group. Only three studies recorded three slight adverse events. There were no reports of severe complications. In conclusions, this study demonstrated that BTA has the potential to improve facial scars with an acceptable safety profile.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.798
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.146
GPT teacher head0.460
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations17
Published2022
Admission routes1
Has abstractyes

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