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Record W2120439036 · doi:10.5021/ad.2014.26.5.615

Comparison of the Effectiveness of Nonablative Fractional Laser versus Pulsed-Dye Laser in Thyroidectomy Scar Prevention

2014· article· en· W2120439036 on OpenAlexaboutno aff
Ji Min Ha, Han Su Kim, Eun Byul Cho, Gyeong‐Hun Park, Eun Joo Park, Kwang Ho Kim, Lee Su Kim, Kwang Joong Kim

Bibliographic record

VenueAnnals of Dermatology · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineThyroidectomyDermatologyLaserSurgeryThyroidInternal medicineOptics

Abstract

fetched live from OpenAlex

BACKGROUND: The anterior neck is the site of open thyroidectomy and where postoperative scarring can cause distress to patients. Both fractional and pulsed-dye lasers are effective and safe methods for preventing and improving surgical scars. OBJECTIVE: This study evaluated the improvement in scar appearance with laser intervention during the wound healing process. We evaluated the effect of nonablative fractional and pulsed-dye lasers on fresh thyroidectomy scars. METHODS: Patients were treated 3 times at 4-week interval with a follow-up visit at the 6(th) month. Scars were divided into 2 halves for each optional treatment. At every visit, a questionnaire evaluating the scar and patient satisfaction was completed. RESULTS: Thirty patients completed the 6-month process. The mean Vancouver Scar Scale scores improved significantly from 8.0 to 4.6 and 8.2 to 4.7 with nonablative fractional and pulsed-dye lasers, respectively (p<0.001). However, there was no significant difference between the 2 methods (p=0.840). CONCLUSION: There remains no consensus on the optimal treatment of scars. The present study indicates nonablative fractional and pulsed-dye lasers significantly improve scars. Nonablative fractional lasers are non-inferior to pulsed-dye lasers. Further studies are required to corroborate this finding.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.080
GPT teacher head0.434
Teacher spread0.354 · 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

Citations40
Published2014
Admission routes1
Has abstractyes

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