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Record W2240286486 · doi:10.1177/229255031302100402

A survey of current practices in the diagnosis of and interventions for inhalational injuries in Canadian burn centres

2013· article· en· W2240286486 on OpenAlexaffvenueabout
Justin Yeung, Leslie Tze Fung Leung, Anthony Papp

Bibliographic record

VenueCanadian Journal of Plastic Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsMedicineInhalationIntensive careAnesthesiaResuscitationEmergency medicineSurgeryIntensive care medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To summarize current Canadian practice patterns in the diagnosis of and interventions for inhalation injuries (INHI). METHODS: A 10-question survey regarding the diagnosis of and interventions for INHI was sent to the medical directors of all 16 burn centres across Canada. RESULTS: The response rate to the survey was 50%. Fibreoptic bronchoscopy is required for the diagnosis of INHI in only four centres (50%). The departments of intensive care, plastic surgery, otolaryngology and respirology are involved in performing fibreoptic bronchoscopy in 87.5%, 37.5%, 12.5% and 12.5% of Canadian burn centres, respectively. Intubation for INHI is most often based on physical examination results (87.5%) and clinical history (75%). The most common physical features believed to be most consistent with INHI are dyspnea (87.5%) and hoarseness (87.5%). Common treatments include intubation (87.5%), routine ventilatory support (87.5%) and chest physiotherapy (75%). None of the centres used nebulized heparin. A total of five centres (62.5%) routinely changed the fluid resuscitation protocol when INHI was diagnosed. Only two centres (25%) routinely used prophylactic antibiotics for INHI. CONCLUSION: Prospective, multicentre trials are needed to generate evidence-based consensus in the areas of diagnosis, grading and treatment for INHI in Canada.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
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 head0.115
GPT teacher head0.349
Teacher spread0.234 · 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.

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

Citations11
Published2013
Admission routes3
Has abstractyes

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