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Record W1267912287 · doi:10.7759/cureus.316

Infant Trauma Management in the Emergency Department: An Emergency Medicine Simulation Exercise

2015· article· en· W1267912287 on OpenAlexafffund
Sarah Mathieson, D. Joel Whalen, Adam Dubrowski

Bibliographic record

VenueCureus · 2015
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsMedicineSession (web analytics)Pediatric emergency medicineMedical emergencyEmergency departmentMajor traumaAccident and emergencyEmergency medicineEmergency physicianNursing

Abstract

fetched live from OpenAlex

In a trauma situation, it is essential that emergency room physicians are able to think clearly, make decisions quickly and manage patients in a way consistent with their injuries. In order for emergency medicine residents to adequately develop the skills to deal with trauma situations, it is imperative that they have the opportunity to experience such scenarios in a controlled environment with aptly timed feedback. In the case of infant trauma, sensitivities have to be taken that are specific to pediatric medicine. The following describes a simulation session in which trainees were tasked with managing an infantile patient who had experienced a major trauma as a result of a single vehicle accident. The described simulation session utilized human patient simulators and was tailored to junior (year 1 and 2) emergency medicine residents.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.132
GPT teacher head0.436
Teacher spread0.304 · 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 designSimulation or modeling
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

Citations3
Published2015
Admission routes2
Has abstractyes

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