A comparison of live tissue training and high-fidelity patient simulator
Bibliographic record
Abstract
BACKGROUND: Trauma procedural and management skills are often learned on live tissue. However, there is increasing pressure to use simulators because their fidelity improves and as ethical concerns increase. We randomized military medical technicians (medics) to training on either simulators or live tissue to learn combat casualty care skills to determine if the choice of modality was associated with differences in skill uptake. METHODS: Twenty medics were randomized to trauma training using either simulators or live tissue. Medics were trained to perform five combat casualty care tasks (surgical airway, needle decompression, tourniquet application, wound packing, and intraosseous line insertion). We measured skill uptake using a structured assessment tool. The medics also completed exit questionnaires and interviews to determine which modality they preferred. RESULTS: We found no difference between groups trained with live tissue versus simulators in how they completed each combat casualty care skill. However, we did find that the modality of assessment affected the assessment score. Finally, we found that medics preferred trauma training on live tissue because of the fidelity of tissue handling in live tissue models. However, they also felt that training on simulators also provided additional training value. CONCLUSION: We found no difference in performance between medics trained on simulators versus live tissue models. Even so, medics preferred live tissue training over simulation. However, more studies are required, and future studies need to address the measurement bias of measuring outcomes in the same model on which the study participants are trained. LEVEL OF EVIDENCE: Therapeutic/care management study, level II.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".