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Record W4205545928 · doi:10.1111/ans.17435

Variation in burn wound management approaches for paediatric burn patients in Australia and New Zealand

2022· article· en· W4205545928 on OpenAlexaff
Monica Perkins, Fiona Wood, Bronwyn Griffin, Eduardo Gus, Bernard Carney, Warwick J. Teague, Lincoln M. Tracy

Bibliographic record

VenueANZ Journal of Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersHelen Macpherson Smith TrustAustralasian Foundation for Plastic SurgeryThyne Reid FoundationAustralian Commission on Safety and Quality in Health CareHCF Research Foundation
KeywordsMedicineBurn injuryBurn woundContext (archaeology)Burn unitsRetrospective cohort studyEmergency medicineSurgeryWound healing

Abstract

fetched live from OpenAlex

BACKGROUND: To date, no large-scale exploration of the profile of, and variance among paediatric patients who underwent a burn wound management procedure in theatre exists in an Australian and New Zealand context. This study aims to provide a profile of paediatric burn patients who underwent a burn wound management procedure in theatre during an acute admission and highlight specific areas of practice where there is variation between burn services that may affect treatment efficacy and efficiency. METHODS: We performed a retrospective review of all paediatric patients (ages <16 years) who sustained a burn injury between July 2016 and June 2019 and underwent a burn wound management procedure in theatre, using data from the Burns Registry Australia New Zealand. RESULTS: The number of patients across burn services decreased as TBSA increased. Deep dermal burns represented the majority of cases across services. There was significant variation in time from injury to admission and the proportion of patients who received skin grafts across services. CONCLUSIONS: Significant differences in the patient profile and clinical practices were observed among burn services. A greater understanding of the factors underlying the variations at each particular service will also be helpful.

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)0.001
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 score0.012
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.085
GPT teacher head0.279
Teacher spread0.194 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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