Efficacy of a Novel Surgical Manikin for Simulating Emergency Surgical Procedures
Bibliographic record
Abstract
The practical component of the Advanced Trauma Life Support (ATLS®) course typically includes a TraumaMan® manikin. This manikin is expensive; hence, a low-cost alternative (SurgeMan®) was developed in Brazil. Our primary objective was to compare user satisfaction among SurgeMan, TraumaMan, and porcine models during the course. Our secondary objective was to determine the user satisfaction scores for SurgeMan. This study included 36 ATLS students and nine instructors (4:1 ratio). Tube thoracostomy, cricothyroidotomy, pericardiocentesis, and diagnostic peritoneal lavage were performed on all the three models. The participants then rated their satisfaction both after each activity and after the course. The porcine and TraumaMan models fared better than SurgeMan for all skills except pericardiocentesis. In the absence of ethical or financial constraints, 58 per cent of the students and 66 per cent of the instructors indicated preference for the porcine model. When ethical and financial factors were considered, no preference was evident among the students, whereas 66 per cent of instructors preferred SurgeMan over the others. The students gave all three models an overall adequacy rating of >80 per cent; the instructors gave only the animal models an adequacy rating of <80 per cent. Although the users were more satisfied with TraumaMan than with SurgeMan, both were considered acceptable for the ATLS course.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".