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Early excision and grafting versus delayed grafting in deep burns of the hand

2019· article· en· W2976500628 on OpenAlexaboutno aff
Mohammed Leithy Ahmed Badr, Tarek Fouad Keshk, Yahia Mohammed Alkhateeb, Ashraf Moustafa Esmail El Refaai

Bibliographic record

VenueInternational Surgery Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgerySkin graftingMuscle contractureGrafting

Abstract

fetched live from OpenAlex

Background: Deep burns of the hand are considered severe, because even a small wound may cause profound functional disability, ugly scar and psychosocial problems. The aim of the study is to compare early excision and grafting versus delayed grafting in deep burns of the hand.Methods: This study was conducted on 30 patients with deep burns of the hand. Patients were randomly divided in to two equal groups. Group I included 15 patients who were subjected to early excision and grafting within the first week after injury while group II included 15 patients who were subjected to delayed excision and grafting two weeks after injury. The study was conducted on patients presented to Plastic and Reconstructive Surgery Department of Abou Qir General Hospital in the period from December 2016 to December 2017.Results: The results of early excision and grafting were better than delayed grafting regarding the intake, infection and post-burn contraction (mean 91.33±7.67 in group I and 83.67±10.08 in group II with p value=0.026).Conclusions: Early excision and grafting of the hand is a better alternative than delayed excision and grafting as regards better graft intake, less wound infection, less contractures, less risk of regrafting, less hospital stay, less 5D-itching scale and Vancouver score and more cost effectiveness.

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 score0.013
Threshold uncertainty score0.185

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 head0.026
GPT teacher head0.297
Teacher spread0.271 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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