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Record W3113095390 · doi:10.1007/978-3-030-44766-3_60

Burn Hypertrophic Scar in Pediatric Patients: Clinical Case

2020· book-chapter· en· W3113095390 on OpenAlexaff
Roohi Vinaik, Joel Fish, Marc G. Jeschke

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoSunnybrook Hospital
Fundersnot available
KeywordsMedicineHypertrophic scarHypertrophic scarsScarsErythemaSurgeryPopulationDermatology

Abstract

fetched live from OpenAlex

Abstract Recent improvements in burn care have resulted in greater patient survival of severe burns. With improved survival, treatment of the resulting permanent burn hypertrophic scars requires extensive care. Hypertrophic scarring occurs due to aberrations in the normal healing process, resulting in excessive inflammation and collagen deposition at the site of injury. These scars are accompanied by symptoms such as pain, pruritus, erythema, and limited mobility. The high scar prevalence in pediatric patients and accompanying physical, psychological, and social burden warrant a better understanding of the possible treatment options. Currently, several therapeutic strategies exist for hypertrophic scar management in the pediatric patient, although none are completely effective. Recently, laser therapy has emerged as a potential therapy for symptomatic relief and scar modulation. Here, we provide an up-to-date review of treatment options for hypertrophic scars in the pediatric population. In addition, we discuss a clinical case, outlining the potential merits of addition of laser therapy and surgical revision for the treatment of 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.730
Threshold uncertainty score1.000

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.075
GPT teacher head0.352
Teacher spread0.277 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2020
Admission routes1
Has abstractyes

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