Current use of live tissue training in trauma: a descriptive systematic review
Bibliographic record
Abstract
BACKGROUND: Growing public concern for animal welfare, advances in computerized simulation and economic barriers have drawn a critical eye to the use of live tissue training (LTT) in trauma skills acquisition. As a consequence, other simulation methods have replaced LTT, for example, in the Advanced Trauma Life Support (ATLS) course. Owing to the lack of clear conclusions in the literature, we conducted a systematic review to determine the value of LTT alone and in comparison to other simulation methods in trauma. METHODS: We performed a systematic review of the literature considering observational studies and randomized controlled trials (RCTs) that examined LTT in trauma exclusively or compared with other simulation methods. Independently and in duplicate, we adjudicated studies for inclusion and data abstraction. We assessed the quality and risk of bias. RESULTS: Twelve studies met our inclusion criteria: 2 RCTs and 10 prospective cohort studies. Eight and 4 studies were performed in the military and in the civilian settings, respectively. Anesthetized swine were used in 8 studies and goats in 1. The cohort studies involved LTT alone. Different adjunctive training modalities were included: mannequins in 6 studies, cadavers in 2, computer simulation in 1, video presentations in 2 and wound moulage scenarios in 1. The overall methodological quality was moderate as per the Newcastle-Ottawa score (mean 6.0 ± 0, possible range 1-9). The 2 RCTs did not demonstrate adequate random sequence generation and allocation concealment. CONCLUSION: There is limited evidence that other types of simulation are better than LTT. Data on training effects of LTT versus other simulations on outcomes are lacking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".