Efficacy of a Novel Surgical Manikin for Simulating Emergency Surgical Procedures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 it