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Record W1973766497 · doi:10.1097/prs.0000000000000416

Botulinum Toxin to Improve Results in Cleft Lip Repair

2014· article· en· W1973766497 on OpenAlexaboutno aff
Chun-Shin Chang, Christopher Glenn Wallace, Yen-Chang Hsiao, Chee‐Jen Chang

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsScarsMedicineBotulinum toxinVascularityRandomized controlled trialOrbicularis oris muscleVisual analogue scaleSurgeryDentistryAnatomyUpper lip

Abstract

fetched live from OpenAlex

BACKGROUND: Upper lip wounds that lie perpendicular to the relaxed skin tension lines are subjected to repetitive dynamic tension caused by the orbicularis oris muscle and are susceptible to unsatisfactory scarring. METHODS: In this double-blind, randomized, vehicle-controlled, prospective trial, 60 consecutive patients with unilateral cleft lip undergoing primary cheiloplasties between August of 2011 and June of 2012 were randomized to receive botulinum toxin type A or vehicle injections into the subjacent orbicularis oris muscle immediately after wound closure. Scars were assessed after 6 months using the Vancouver Scar Scale, photographic visual analogue scale, and photographic scar width measurements. RESULTS: Fifty-nine patients completed the trial. Measurements of scar widths at two defined points revealed significantly better visual analogue scale scores and narrower scars in the experimental group. However, Vancouver Scar Scale assessments were similar between groups. CONCLUSIONS: Botulinum toxin injections into the subjacent orbicularis oris muscle produced better appearing and narrower cheiloplasty scars, but provided no additional benefits in terms of scar pigmentation, vascularity, pliability, or height. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations98
Published2014
Admission routes1
Has abstractyes

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