MétaCan
Menu
Back to cohort
Record W3024659104

Electrophotobiomodulation in the treatment of facial post-burn hypertrophic scars in pediatric patients.

2018· article· en· W3024659104 on OpenAlexaboutno aff
Nader Elmelegy, Ahmed M. Hegazy, Mohamed Saad Sadaka, Doaa E. Abdeldaim

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHypertrophic scarHypertrophic scarsScarsTreatment modalitySurgeryPediatric burnIntense pulsed lightDermatology
DOInot available

Abstract

fetched live from OpenAlex

Hypertrophic scar continues to be one of the leading reasons for surgical and non-surgical treatments after burn healing. Facial post-burn hypertrophic scars can cause severe functional and emotional disability, as they are usually difficult to conceal. Numerous nonsurgical and surgical therapies have been used for the treatment of hypertrophic scars. This study describes the combination of bipolar radiofrequency, intense pulsed light and cooling (given the collective term 'E-light'), and reports the outcomes of its use in the treatment of post-burn facial hypertrophic scars in a series of sixty-five patients in the pediatric age group. There were no reports in the literature of the use of this modality (E-light) in the treatment of facial post-burn hypertrophic scars in pediatric patients. Results showed that the mean decrease in total VSS score for all patients was 5.8. Regarding the satisfaction of the parents of our patients, 66.15% rated the result excellent, 24.61% rated it good and 9.23% rated it fair. We received no poor ratings for the final result, with a significant reduction in total Vancouver scar scale after treatment (P-value = 0.000). The E-light therapy technique studied in this work is effective, safe and economical if compared to other treatment modalities that can be used in the management of facial post-burn hypertrophic scars.

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 score0.134
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.023
GPT teacher head0.268
Teacher spread0.245 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicDermatologic Treatments and ResearchFrench-language works237,207