MétaCan
Menu
Back to cohort
Record W2582034202 · doi:10.1002/lsm.22634

Early use of CO<sub>2</sub> lasers and silicone gel on surgical scars: Prospective study

2017· article· en· W2582034202 on OpenAlexaboutno aff
Luiz Ronaldo Alberti, Eduardo Faria Vicari, Roselaine De Souza Jardim Vicari, Andy Petroianu

Bibliographic record

VenueLasers in Surgery and Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsSurgerySiliconeProspective cohort studyLaserStatistical significanceOpticsInternal medicineMaterials science

Abstract

fetched live from OpenAlex

Introduction Some publications have shown good aesthetic results for scars through the early application of fractional CO 2 lasers on elective surgery scars. The aim of this randomized, double‐blinded clinical trial was to compare the aesthetic quality of the scar from a group of patients submitted to super‐pulsed fractional CO 2 laser applications (10,600 nm fractional CO 2 , set at a density of 20% and an energy of 10 mJ, a scanner of 03 × 03 mm, and a pulse repetition time of 0.3 seconds) in contrast with the other group that used only the silicone gel on the scar after plastic surgery. Method A prospective study was conducted by analyzing 42 patients with recent scars of up to three weeks in patients with a I–IV Fitz‐Patrick skin phototype. The scars were evaluated aesthetically in the second and sixth months by applying the Vancouver scale. Results At 2 months of treatment, the statistical data showed a discrete superiority in the LASER group's treatment, as compared to that of the SILICONE group, in both percentage and significance concerning flexibility ( P = 0.05) and pigmentation ( P = 0.01). Laser group presented better results in the sixth month ( P = 0,03). Conclusion The early use of the fractional CO 2 laser contributed to improving the aesthetic quality of scars from elective surgeries in the second and in the 6th months. Lasers Surg. Med. 49:570–576, 2017. © 2017 Wiley Periodicals, Inc.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.348
Teacher spread0.287 · 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 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

Citations18
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueLasers in Surgery and MedicineSame topicDermatologic Treatments and ResearchFrench-language works237,207