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Record W3007152624 · doi:10.1093/milmed/usz482

Use of a Digital Cognitive Aid in the Early Management of Simulated War Wounds in a Combat Environment, a Randomized Trial

2020· article· en· W3007152624 on OpenAlexaboutno aff
Michael Truchot, Baptiste Balança, Pierre François Wey, Karim Tazarourte, François Lecomte, Arnaud Le Goff, Simon Leigh-Smith, Jean-Jacques Lehot, Thomas Rimmelé, Jean Christophe Cejka

Bibliographic record

VenueMilitary Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialMedicineMedical emergencyPsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: The French army has implemented an algorithm based on the acronym "MARCHE RYAN," each letter standing for a key action to complete in order to help first care providers during emergency casualty care. On the battlefield, the risk of error is increased, and the use of cognitive aids (CAs) might be helpful to avoid distraction. We investigated the effect of using a digital CA (MAX, for Medical Assistance eXpert) by combat casualty care providers on their technical and nontechnical performances during the early management of simulated war wounds, compared to their memory and training alone. MATERIALS AND METHODS: We conducted a randomized, controlled, unblinded study between July 2016 and February 2017. This study was approved by the Ethics Committee of the Ethical Board of Desgenettes Army Training Hospital (14.06.2017 n°385) and was registered on clinicaltrials.gov (NCT03483727). It took place during medicalization training in hostile environment ("MEDICHOS") in Chamonix Mont-Blanc and in the first aid training center in La Valbonne military base (France). Each participant had to deal with two different scenarios, one with MAX (MAX+) and the other without (MAX-). Scenarios were held using either high-fidelity patient simulators or actors as wounded patients. The primary outcome was participants' technical performance rated as their adherence to the MARCHE RYAN procedure (maximum 100%). The secondary outcome was the nontechnical performance according to the Ottawa crisis resource management Global Rating Scale (maximum 42). RESULTS: Technical performance was significantly higher in the MAX+ scenarios (70.60 IQR [63.70-73.56] than in the MAX- scenarios (56.25 IQR [52.88-62.09], p = 0.002). The Ottawa scores were significantly higher in the MAX+ scenarios (31.50 IQR [29.50-33.75]) than in the MAX- scenarios (29.50 IQR [24.50-32.00], p = 0.031). CONCLUSIONS: The use of a digital CA by combat casualty care providers improved technical and nontechnical performances during field training of simulated crises. Following recommendations on the design and use of CA, regular team training would improve fluidity in the use and acceptance of an aid, by a highly drilled professional corporation with a strong culture of leadership. Digital CA should be tested at a larger scale in order to validate their contribution to real combat casualty care.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.080
GPT teacher head0.333
Teacher spread0.253 · 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 designRandomized trial
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

Citations9
Published2020
Admission routes1
Has abstractyes

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