Realism of procedural task trainers in a pediatric emergency medicine procedures course
Bibliographic record
Abstract
BACKGROUND: Pediatric emergency medicine (PEM) physicians have minimal experience in life saving procedures and have turned to task trainers to learn these skills. Realism of these models is an important consideration that has received little study. METHOD: PEM physicians and trainees participated in a day long procedural training course that utilized commercially available and homemade task trainers to teach pericardiocentesis, chest tube insertion, cricothyroidotomy and central line insertion. Participants rated the realism of the task trainers as part of a post-course survey. RESULTS: The homemade task trainers received variable realism ratings, with 91% of participants rating the pork rib chest tube model as realistic, 82% rating the gelatin pericardiocentesis mold as realistic and 36% rating the ventilator tubing cricothyroidotomy model as realistic. Commercial trainers also received variable ratings, with 45% rating the chest drain and pericardiocentesis simulator as realistic, 74% rating the crichotracheotomy trainer as realistic and 80% rating the central line insertion trainer as realistic. CONCLUSIONS: Task training models utilized in our course received variable realism ratings. When deciding what type of task trainer to use future courses should carefully consider the desired aspect of realism, and how it aligns with the procedural skill, balanced with cost considerations.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".