Teaching Emergency Surgical Skills for Trauma Resuscitation-Mechanical Simulator versus Animal Model
Bibliographic record
Abstract
Background. Traditionally, surgical skills in trauma resuscitation have been taught using animal models in the advanced trauma life support (ATLS) course. We compare one mechanical model (TraumaMan simulator) as an alternative teaching tool for these skills.Method. Eighteen providers and 14 instructors performed four surgical procedures on TraumaMan and compared educational effectiveness with the porcine model. Evaluation was conducted (Likert system 1: very poor to 5: excellent). The participants indicated if TraumaMan was a suitable (scale 1: not suitable to 4: excellent) ATLS teaching model considering cost, animal ethics concerns, and other factors. Comments were solicited for both models.Results. Overall ratings for educational effectiveness of the 4 skills ranged from 3.58 to 4.36 for the porcine and 3.48 to 4.29 for the TraumaMan model. TraumaMan suitability was rated 3-4 (mean 3.38) by 84% participants. TraumaMan as a substitute for the porcine model was recommended by 85% participants. With no ethical or cost concerns, 44% students and 71% instructors preferred TraumaMan. Considering all factors, TraumaMan was preferred by 78% students and 93% instructors.Conclusions. TraumaMan is a suitable alternative to the porcine model and considering all factors it may be the preferred method for teaching ATLS emergency trauma surgical skills.
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 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 | 0.004 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".