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Record W2155613786 · doi:10.1097/bcr.0b013e318278906d

Nonsurgical Scar Management of the Face

2013· article· en· W2155613786 on OpenAlexaboutno aff
Ingrid Parry, Soman Sen, Tina L. Palmieri, David A. Greenhalgh

Bibliographic record

VenueJournal of Burn Care & Research · 2013
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
FundersUniversity of California, Davis
KeywordsMedicinePARRYPediatrics

Abstract

fetched live from OpenAlex

Special emphasis is placed on the clinical management of facial scarring because of the profound physical and psychological impact of facial burns. Noninvasive methods of facial scar management include pressure therapy, silicone, massage, and facial exercises. Early implementation of these scar management techniques after a burn injury is typically accepted as standard burn rehabilitation practice, however, little data exist to support this practice. This study evaluated the timing of common noninvasive scar management interventions after facial skin grafting in children and the impact on outcome, as measured by scar assessment and need for facial reconstructive surgery. A retrospective review of 138 patients who underwent excision and grafting of the face and subsequent noninvasive scar management during a 10-year time frame was conducted. Regression analyses were used to show that earlier application of silicone was significantly related to lower Modified Vancouver Scar Scale scores, specifically in the subscales of vascularity and pigmentation. Early use of pressure therapy and implementation of facial exercises were also related to lower Modified Vancouver Scar Scale vascularity scores. No relationship was found between timing of the interventions and facial reconstructive outcome. Early use of silicone, pressure therapy, and exercise may improve scar outcome and accelerate time to scar maturity.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.064
GPT teacher head0.416
Teacher spread0.352 · 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

Citations48
Published2013
Admission routes1
Has abstractyes

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