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Record W2798052430 · doi:10.1093/jbcr/iry006.235

313 Dermatome-Induced Lacerations: Incidence, Management, and Preventive Measures

2018· article· en· W2798052430 on OpenAlexaboutno aff
Francesco M. Egro, Ololade T. Saliu, A Corcos, Jenny Ziembicki

Bibliographic record

VenueJournal of Burn Care & Research · 2018
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDermatomeMedicineNeurovascular bundleIncidence (geometry)ComplicationSurgery

Abstract

fetched live from OpenAlex

Dermatome-induced lacerations are a known complication; however, there is a paucity of literature discussing the incidence and predisposing factors. The aim of this study was to determine the incidence and risk factors in order to develop a preventive algorithm. An 18-question survey was sent to all U.S. and Canadian burn unit directors. Surgeons were queried about type and location of their practices, average annual caseload of skin graft harvesting, and number of dermatome-induced lacerations. The survey also asked about donor site location, harvesting technique and equipment, laceration severity, and causative factors. An algorithm was developed based on the results and the current manufacturers’ guidelines. Fifty-six responses (42% response rate) were received from the burn unit directors. They reported 133 lacerations over the past 5 years. The overall incidence of dermatome-induced lacerations was 0.1% per year (1.3 per 1,000 cases). The most commonly attributed causes were excessive pressure (25.0%) and patient factors (18.4%). Most lacerations occurred when using air dermatomes (73.0%) with a 4-inch guard (63.5%), 0.010-0.015-inch thickness (78.4%), and 30–45° angulation (47.3%); the most common brand was Zimmer (71.6%). The dermatome was typically set up by a scrub tech/nurse (48.6%), while the skin harvesting was performed by residents (39.2%) or attendings (35.1%). Lacerations typically extended to subcutaneous tissue (70.3%), with no neurovascular injury (86.5%). Our study showed that dermatome-induced lacerations are rare events and that certain factors predispose patients to injury. An algorithm was developed to provide guidance on risk factor identification and the set up and use of dermatomes. This study provides the only evidence on the incidence, etiology and risk factors of dermatome-induced lacerations. The development of an algorithm will help to increase the safety of skin grafting, the most commonly performed procedure in burn surgery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.454
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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