The Value of Live Tissue Training for Combat Casualty Care: A Survey of Canadian Combat Medics With Battlefield Experience in Afghanistan
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".