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Effects of Enhanced Patient Education on Compliance With Silicone Gel Sheeting and Burn Scar Outcome: A Randomized Prospective Study

2003· article· en· W2085965132 on OpenAlexaffabout
Ken So, Nisha Umraw, Jeff Scott, Melinda A. Musgrave, Robert Cartotto

Bibliographic record

VenueJournal of Burn Care & Rehabilitation · 2003
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsWomen's College Hospital
Fundersnot available
KeywordsMedicineScarsRandomized controlled trialHypertrophic scarSiliconeHypertrophic scarsSurgeryAnesthesia

Abstract

fetched live from OpenAlex

The purpose of this study was to determine whether enhanced patient education increases compliance with silicone gel sheeting (SGS) on hypertrophic (HT) scars and to determine whether this results in any improvement in scar outcome. Outpatients with a HT burn scar were randomized to either a conventional education group (CEG), which received routine instruction on the use of SGS or to an enhanced education Group (EEG), which also received routine instruction, along with a detailed 5-page handout and a 26-minute videotape. The CEG (n = 12, 67% male, age 38 +/- 10 years) and the EEG (n = 13, 77% male, age 47 +/- 10 years) were followed monthly for 6 months. Subjects in the EEG wore SGS for 21.8 +/- 3.0 hr/day compared with only 10.1 +/- 7.5 hr/day of use in the CEG (P <.001). Scars in the EEG had significantly better Vancouver Scar Scale ratings for pigmentation (P =.02), height (P =.03), and pliability (P =.02) by 6 months. Patients in the EEG had significantly better subjective ratings for the parameters of scar itch (P =.01), color (P =.02), hardness (P =.01), and elevation (P =.01). Finally, scars in the EEG had significantly better ratings for border height (P =.002) and thickness (P =.01) at 6 months based on evaluation of digital photographs. Detailed multimedia patient education improves compliance with SGS and results in a better scar outcome.

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)0.002
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 score0.104
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.009
GPT teacher head0.321
Teacher spread0.312 · 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

Citations62
Published2003
Admission routes2
Has abstractyes

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