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Record W2909066026 · doi:10.12968/jowc.2019.28.1.24

Treatment of partial-thickness burns with a tulle-gras dressing and a hydrophilic polyurethane membrane: a comparative study

2019· article· en· W2909066026 on OpenAlexaboutno aff
Ci̇han Şahi̇n, Pinar Kaplan, Sinan Öztürk, Sule Alpar, Hüseyın Karagöz

Bibliographic record

VenueJournal of Wound Care · 2019
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSignificant differenceWound dressingSurgerySkin thicknessDermatologyInternal medicineComposite material

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this revisited study was to compare the clinical efficacy and long-term scar evaluation of a hydrophilic polyurethane membrane (HPM), Omiderm (Omikron Scientific Ltd., Rehovot, Israel) and an antimicrobial tulle-gras dressing (TGD), Bactigras (Smith & Nephew) in the management of partial-thickness burns. METHOD: Patients with partial-thickness burns were enrolled in this prospective study. Burn areas were divided into two areas and both dressings were applied to each field at the same time. Time to full re-epithelialisation and scar evaluation were compared using the Vancouver Scar Scale (VSS). RESULTS: A total of 21 patients, mean age 36.8 years, with 22 burns areas participated. The results showed that there is no statistically significant difference in terms of full epithelialisation time in the application of either dressing (p>0.05). However, with deep dermal burns, the HPM provided slightly faster epithelialisation (p>0.05). A VSS assessment showed no statistically significant difference (p>0.05) between applying either dressing materials. CONCLUSION: This study indicated that both dressings had the same effectiveness in treatment of partial-thickness burn wounds. However, the use of the HPM, especially in deep dermal second-degree burns, should be one of the first-line clinical choices, based on the advantages discerned by this study.

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 score0.489
Threshold uncertainty score0.475

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 integrity0.0000.000
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 head0.030
GPT teacher head0.328
Teacher spread0.298 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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