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Record W2098981335 · doi:10.3346/jkms.2014.29.s3.s249

Prevention of Postsurgical Scars: Comparsion of Efficacy and Convenience between Silicone Gel Sheet and Topical Silicone Gel

2014· article· en· W2098981335 on OpenAlexaboutno aff
Sue Min Kim, Jung-Sik Choi, Jung Ho Lee, Young Jin Kim

Bibliographic record

VenueJournal of Korean Medical Science · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsSiliconeMedicineScarsSurgeryMaterials science

Abstract

fetched live from OpenAlex

To date, few studies have compared the effectiveness of topical silicone gels versus that of silicone gel sheets in preventing scars. In this prospective study, we compared the efficacy and the convenience of use of the 2 products. We enrolled 30 patients who had undergone a surgical procedure 2 weeks to 3 months before joining the study. These participants were randomly assigned to 2 treatment arms: one for treatment with a silicone gel sheet, and the other for treatment with a topical silicone gel. Vancouver Scar Scale (VSS) scores were obtained for all patients; in addition, participants completed scoring patient questionnaires 1 and 3 months after treatment onset. Our results reveal not only that no significant difference in efficacy exists between the 2 products but also that topical silicone gels are more convenient to use. While previous studies have advocated for silicone gel sheets as first-line therapies in postoperative scar management, we maintain that similar effects can be expected with topical silicone gel. The authors recommend that, when clinicians have a choice of silicone-based products for scar prevention, they should focus on each patient's scar location, lifestyle, and willingness to undergo scar prevention treatment.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.292
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.033
GPT teacher head0.361
Teacher spread0.329 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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