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Record W3161160150 · doi:10.1111/jocd.14238

Evaluating outcomes of pulsed dye laser therapy combined with intralesional triamcinolone injection after surgical removal of hypertrophic cesarean section scars

2021· article· en· W3161160150 on OpenAlexaboutno aff
Jee Woo Kim, Chang‐Hun Huh, Jung‐Im Na, J. S. Hong, Jee Yoon Park, Jung Won Shin

Bibliographic record

VenueJournal of Cosmetic Dermatology · 2021
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHypertrophic scarScarsTriamcinolone acetonideHypertrophic scarsSurgeryKeloidDye laserLaser

Abstract

fetched live from OpenAlex

BACKGROUND: Recently, pulsed dye laser (PDL) combined with triamcinolone intralesional injection (TAILI) has been introduced for surgical scar prevention. However, little is known about this procedure's effectiveness in preventing hypertrophic scar following surgical scar removal. OBJECTIVES: This study aimed to evaluate the outcome of early intervention using PDL combined with TAILI after surgical removal of hypertrophic cesarean section (CS) scars. METHODS: The medical records of 35 patients who underwent early intervention using PDL and TAILI after removal of hypertrophic CS scars were retrospectively reviewed. The scars' average Vancouver Scar Scale (VSS) scores before scar removal and 3 months after the final treatment were compared. RESULTS: The patients received 4.23 treatments on average and were followed up for a mean period of 7.74 months. The mean final VSS was 3.11 ± 1.52 and was significantly lower than that of the previous VSS (9.29 ± 1.74, p = 0.000). VSS of the previous CS scar, and the presence or absence of keloid formation in other areas, was associated with treatment outcome (p = 0.003 and 0.008, respectively). CONCLUSIONS: Early intervention using PDL combined with TAILI could prevent the recurrence or progression of hypertrophic CS scarring after surgical scar removal.

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 score0.366
Threshold uncertainty score0.413

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 head0.047
GPT teacher head0.356
Teacher spread0.309 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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