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Record W2090314469 · doi:10.1111/dsu.12472

Prevention of Thyroidectomy Scars in Korean Patients Using a New Combination of Intralesional Injection of Low-Dose Steroid and Pulsed Dye Laser Starting within 4 Weeks of Suture Removal

2014· article· en· W2090314469 on OpenAlexaboutno aff
Han-Won Ryu, Jihyoung Cho, Kyu‐Suk Lee, Jae-We Cho

Bibliographic record

VenueDermatologic Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsScarsMedicineFibrous jointThyroidectomySurgeryPatient satisfactionThyroidInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Regulation of inflammation during the wound healing process reduces scar formation at the injury site. OBJECTIVE: To evaluate the effect of intralesional injection of low-dose steroid with pulsed dye laser on healing of early postoperative thyroidectomy scars. MATERIALS AND METHODS: Twenty Korean women with thyroidectomy scars were enrolled. All were treated with an intralesional injection of low-dose steroid (2 mg/mL) and 595-nm pulsed dye laser starting within 4 weeks of suture removal. The Vancouver Scar Scale (VSS), Global Assessment Score (GAS), and Patient Satisfaction Score were used in this evaluation. RESULTS: Average VSS scores were significantly lower after treatment. The GAS also indicated better cosmetic outcomes after steroid injection in the laser treatment group than after laser treatment only. CONCLUSION: Early postoperative intralesional injection of low-dose steroid and pulsed dye laser treatment is effective and safe.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.029
GPT teacher head0.290
Teacher spread0.261 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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