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Record W2198277544 · doi:10.12968/jowc.2015.24.12.600

Intraoperative and delayed wound approximation in closure of skin defects in different areas

2015· article· en· W2198277544 on OpenAlexaff
Yasser Abdallah Aboelatta, A. Elshahm, Mohamed A. Saleh, Iman Kamel, Hazem M. Aly

Bibliographic record

VenueJournal of Wound Care · 2015
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsSaint John Regional Hospital
Fundersnot available
KeywordsMedicineSurgeryScarsWound closureWound dehiscenceWound healingScalpThighChronic woundDehiscence

Abstract

fetched live from OpenAlex

OBJECTIVE: Wound approximation device is an interesting reconstructive option but not well popularised. In this study we present a simple device that can be used for immediate or delayed closure of large dermal wounds in different anatomical areas. METHOD: Patients with acute and chronic wounds were recruited and underwent immediate intra-operative wound approximation and/or delayed wound approximation, with a home-made wound approximation device. RESULTS: Approximation time in the immediate closure group ranged from 20-140 minutes. Satisfactory scars were obtained in 19 patients (76%) and adherent scars developed in 6 patients. Delayed wound approximation was used successfully in closure of 9 defects. CONCLUSION: This simple dermal wound approximation device can be used intraoperatively to successfully close large difficult wounds, located on the trunk and thigh, with minimal complications. The device can also be used to approximate delayed wounds located in regions where closure is particularly problematic, like the lower leg, foot, and scalp. Some modifications of the device are needed to improve its safety and efficacy. Wound tension is detrimental to adequate wound healing and tensile strength, another basic principle that should not be overlooked to avoid wound dehiscence. Wound approximation is adding to reconstructive options, not replacing them, and they must always be considered.

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 score0.009
Threshold uncertainty score0.236

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 head0.016
GPT teacher head0.273
Teacher spread0.256 · 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
Published2015
Admission routes1
Has abstractyes

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