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Record W4392005458 · doi:10.46747/cfp.700295

Approach to burn treatment in the rural emergency department

2024· review· en· W4392005458 on OpenAlexaffvenue
Cory Tremblay, Kathryn A. Albrecht, Christiaan C. Sonke, Sanjay Azad

Bibliographic record

VenueCanadian Family Physician · 2024
Typereview
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsThunder Bay Regional Health Sciences CentreDalhousie UniversityUniversity of CalgaryNOSM University
Fundersnot available
KeywordsMedicineEmergency departmentMedical emergencyReferralFirst aidBurn injuryEmergency medicinePresentation (obstetrics)Intensive care medicineSurgeryNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To outline an approach to the assessment and initial management of patients with burns in the rural emergency department setting. Three mnemonics are presented that can be used for both the assessment and the initial management of patients with burns in rural settings. QUALITY OF EVIDENCE: Current and local guidelines compiled by a plastic surgeon were reviewed to develop a systematic approach to the treatment of patients with burns. PubMed and other databases were also searched for current literature on emergency care of patients with burns. MAIN MESSAGE: Burn injuries are a common reason for presentation to the emergency department. However, the care of patients with these injuries can vary substantially depending on geographic location, provider training, and hospital resources. Classification of burns, fluid resuscitation guidelines, dressings and wound care, indications for referral, and pain management are discussed. CONCLUSION: Using a systematic approach may help improve burn injury outcomes for patients and provide practitioners with a step-by-step framework for the management of patients with burns in rural settings.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.061
GPT teacher head0.324
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes2
Has abstractyes

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