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Record W2314057472 · doi:10.1097/bcr.0000000000000028

Ablative Fractional Photothermolysis for the Treatment of Hypertrophic Burn Scars in Adult and Pediatric Patients

2014· article· en· W2314057472 on OpenAlexaboutno aff
Anjay Khandelwal, Miranda Yelvington, Xinyu Tang, Susan Brown

Bibliographic record

VenueJournal of Burn Care & Research · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHypertrophic scarsScarsHypertrophic scarSurgeryProspective cohort studyRetrospective cohort study

Abstract

fetched live from OpenAlex

Many patients develop hypertrophic scarring after a burn injury. Numerous treatment modalities have been described and are currently in practice. Photothermolysis or laser therapy has been recently described as an adjunct for management of hypertrophic burn scars. This study is a retrospective chart review of adult and pediatric patients undergoing fractional photothermolysis at a verified burn center examining treatment parameters as well as pre- and post-Vancouver Scar Scale scores. Forty-four patients underwent fractional photothermolysis during the study period of 8 months. Mean pretreatment score was 7.6, and mean posttreatment score was 5.4. The mean decrease in score was 2.2, which was found to be statistically significant. There were no complications. Fractional photothermolysis is a safe and efficacious adjunct therapy for hypertrophic burn scars. Prospective trials would be beneficial to determine optimal therapeutic strategies.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.038
GPT teacher head0.369
Teacher spread0.332 · 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

Citations54
Published2014
Admission routes1
Has abstractyes

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