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Record W2751320083 · doi:10.7205/milmed-d-16-00271

The Value of Live Tissue Training for Combat Casualty Care: A Survey of Canadian Combat Medics With Battlefield Experience in Afghanistan

2017· article· en· W2751320083 on OpenAlexafffundabout
Michael J. Kim, Ian Torrie, Robert Poisson, Nicholas Withers, Stephen Bjarnason, Luís Teodoro da Luz, Dylan Pannell, Andrew Beckett, Homer Tien

Bibliographic record

VenueMilitary Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsMontreal General HospitalCanadian Armed ForcesDefence Research and Development CanadaSunnybrook Health Science Centre
FundersCanadian Armed Forces
KeywordsPreparednessFirst responderBattlefieldMedicineMilitary medicineTraining (meteorology)Competence (human resources)Military personnelModalitiesMedical emergencyFamily medicinePsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: The optimum method for training military personnel for combat casualty care is unknown. In particular, there is debate regarding the incremental benefit of live animal tissue training (LTT) over inanimate human patient simulators (HPSs). Although both LTT and HPS are currently used for predeployment training, the efficacy of these models has not been established. MATERIALS AND METHODS: Canadian Armed Forces combat medics, deployed to Afghanistan between 2006 and 2011, were surveyed retrospectively regarding their experience with combat casualty care and predeployment training. HPSs were used to prepare these combat medics for early rotations. In later years, personnel received a combination of training modalities including HPS and LTT, using anaesthetized porcine models in accordance with appropriate animal care standards. Among those deployed on multiple rotations, there was a cohort who was prepared for deployment using only HPS training, and who later were prepared using mixed-modality training, which included LTT. We asked these medics to compare their predeployment training using HPS only versus their mixed-modality training in how each training package prepared them for battlefield trauma care. RESULTS: Thirty-eight individuals responded, with 20 respondents deployed on multiple rotations. Respondents performed life-saving skills during 89% of the rotations. Self-perceived competence and preparedness were notably higher after incorporation of LTT than after HPS alone. Of 17 respondents deployed on both early and late rotations, the majority felt the latter training was more worthwhile. In addition, almost all individuals felt that LTT should be added to HPS training. Narrative comments described multiple benefits of adding LTT to other types of training. CONCLUSIONS: Among many experienced Canadian Armed Forces personnel, LTT is considered essential predeployment preparation. Individuals who experienced only HPS training before active duty on their first combat deployment reported feeling more competent on subsequent combat deployments after the addition of live tissue models. IMPACT: There has been a movement away from the use of LTT in preparing combat medics for deployment. This article suggests that we should reconsider any decision to completely exclude Live Tissue Training as part of our training plan for combat medics. RECOMMENDATIONS: Military medical organizations should consider judiciously incorporating LTT with human patient simulation training to prepare combat medics for treating battlefield trauma.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.122
GPT teacher head0.369
Teacher spread0.248 · 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 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

Citations14
Published2017
Admission routes3
Has abstractyes

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