Teaching Lifesaving Procedures: The Impact of Model Fidelity on Acquisition and Transfer of Cricothyrotomy Skills to Performance on Cadavers
Bibliographic record
Abstract
BACKGROUND: A decline in emergency surgical airway procedures in recent years has resulted in a decreased exposure to cricothyrotomy. Consequently, residents have very little experience or confidence in performing this intervention. In this study, we compared cricothyrotomy skills acquired on a simple inexpensive model to those learned on a high fidelity simulator using valid evaluation instruments and testing on cadavers. METHODS: First and second year anesthesiology residents were recruited. All subjects performed a videotaped pretest cricothyrotomy on cadavers. Subjects were randomized into two groups: The high fidelity group (n = 11) performed two cricothyrotomies on a full-scale simulator with an anatomically accurate larynx. The low fidelity group (n = 11) performed two cricothyrotomies on a low fidelity model constructed from corrugated tubing. Within 2 wk all subjects performed a posttest. Two blinded examiners graded and timed the performances using a checklist and a global rating scale. RESULTS: There was no significant difference in the change from pretest to posttest performance between the model groups as evaluated by all three measures (all: P = NS). Training on both models significantly improved performance on all measures (all: P < 0.001). Inter-rater reliability was strong (checklist: r = 0.90; global rating scale: r = 0.89). CONCLUSIONS: Our study shows that a simple inexpensive model achieved the same effect on objectively rated skill acquisition as did an expensive simulator. The skills acquired on both models transferred effectively to cadavers. Training for this life-saving skill does not need to be limited by simulator accessibility or cost.
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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 | 0.003 | 0.012 |
| 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) | 0.002 | 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".