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Record W4317797580 · doi:10.1097/dss.0000000000003688

Optimal Timing of Fractional CO2 Laser on Cleft Lip Scars: A Single-Blind Randomized Controlled Cohort Study

2023· article· en· W4317797580 on OpenAlexaboutno aff
Haoshu Chi, Xinran Zhao, Lingyue Shen, Yiyun Liu, Ming Cai

Bibliographic record

VenueDermatologic Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsLaser treatmentVascularitySurgeryRandomized controlled trialCohortCohort studyLaserInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND Scar is one of the most common complications for patients with cleft lip. Fractional CO2 laser is an effective method to improve the appearance of scars. However, there are no published articles about the optimal time for applying. OBJECTIVE To determine the optimal time to apply a fractional CO 2 laser for the treatment of postsecondary repair scars in patients with cleft lip. METHODS Forty-two patients with linear scarring after cleft lip repair were recruited from November 2021 to October 2022. A single-blind, randomized, controlled cohort study was conducted to examine the impact of fractional CO 2 laser treatment compared with conventional conservative treatment. Thirty patients started laser treatment at 1 month ( n = 10), 3 months ( n = 10), and 6 months ( n = 10) postoperatively, and 12 patients were in the control group. Each patient was treated with high-energy low-density fractional CO 2 laser treatment 3 times at an interval of 1 month. The Vancouver Scar Scale (VSS) was used for scar evaluation to determine vascularity, pigmentation, pliability, and height. RESULTS The VSS scores decreased significantly after laser treatment ( p < .05), with the most significant improvement in scars in the group that started treatment 1 month after the surgery. CONCLUSION Early postoperative fractional CO 2 laser treatment of cleft lip scars is more effective than later treatment.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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)0.0000.000

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 head0.053
GPT teacher head0.324
Teacher spread0.271 · 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 designRandomized trial
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

Citations16
Published2023
Admission routes1
Has abstractyes

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