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A retrospective study of comparison of collagen dressing versus conventional dressing for skin graft donor site

2021· article· en· W3164522708 on OpenAlexaboutno aff
Tushar J. Dave, C. A. Shashirekha, Krupa Krishnaprasad

Bibliographic record

VenueInternational Surgery Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSkin graftingSurgeryDermisRetrospective cohort studyWound healingWound dressingPain scorePathology

Abstract

fetched live from OpenAlex

Background: Split-skin grafting is commonly employed for covering skin defects in case of ulcers, deep burns and following trauma. It involves harvesting of the epidermis and upper 1/3rd of dermis resulting in a wound called donor site wound (DSW). These wounds pose a kind of burden to patients during the process and after the process of wound healing. These wounds tend to cause pain, are at risk of getting infected, pruritis and cosmetic inconvenience. DSW has been managed with closed or open dressings. Out of many methods, we aim to compare the efficacy of collagen dressing with that of conventional dressing in this study.Methods: A retrospective study including 30 subjects were stratified into 2 groups; group A-collagen dressing and group B- conventional dressing. Patients aged between 18 to 60 years undergoing split thickness skin grafting were included. Patients who are immunocompromised, diabetic, with underlying skin disease and infected wounds were excluded. The outcome was compared in terms of pain, pruritis and scar assessment using Vancouver scar scale.Results: In the present study there was significant difference in median pain score, pruritus and median Vancouver scar score in collagen group compared to conventional group at all the intervals. Also, the incidence of surgical site infection was lower in the collagen dressing group.Conclusions: Collagen dressing is superior compared to conventional dressing in terms of lower pain score, pruritus score and Vancouver scar score.

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 score0.025
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.088
GPT teacher head0.405
Teacher spread0.317 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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