Increasing access to simulation opportunities for emergent cricothyrotomy using a 3D model and positive pressure
Bibliographic record
Abstract
BACKGROUND: Inexpensive, accessible models for simulation are essential to prepare providers to perform cricothyrotomy (CT), a rarely performed but critical procedure. We assessed a portable and inexpensive open-sourced 3D printed CT model versus a routine simulation model for training. METHODS: Residents and fellows of an academic Emergency Medicine (EM) program were randomized to complete a training session for CT with a conventional mannequin or the novel CT technique. After a two-week washout period, all participants performed a CT on an animal surrogate while EM attendings, blinded to assignment of participant training session, evaluated participants with a standardized checklist of steps of the technique. The data collected assessed the primary endpoint of successful completion of the checklist, as well as time to ventilation. RESULTS: = 0.42, consistent with noninferiority of the novel technique. For time to ventilation, Group 1 had a median time (MT) of 133 s (IQR 84-165) with Group 2 having a MT of 63 sec (IQR 53-130). CONCLUSION: Our CT training technique demonstrated non-inferiority to a conventional training mannequin with respect to success rate for learners. Furthermore, the time to ventilation was faster in the group trained with the novel model. Coupled with intrinsic economic and logistical advantages over conventional techniques, this model provides an effective means of reinforcing skill in a rare procedure.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".