Simulation of Adult Surgical Cricothyrotomy for Anesthesiology and Emergency Medicine Residents: Adapted for COVID-19
Bibliographic record
Abstract
Introduction: In a CICO (cannot intubate, cannot oxygenate) situation, anesthesiologists and acute care physicians must be able to perform an emergency surgical cricothyrotomy (front-of-neck airway procedure). CICOs are high-acuity situations with rare opportunities for safe practice. In COVID-19 airway management guidelines, bougie-assisted surgical cricothyrotomy is the recommended emergency strategy for CICO situations. Methods: We designed a 4-hour procedural simulation workshop on surgical cricothyrotomy to train 16 medical residents. We provided prerequisite readings, a lecture, and a videotaped demonstration. Two clinical scenarios introduced deliberate practice on partial-task neck simulators and fresh human cadavers. We segmented an evidence-based procedure and asked participants to verbalize the five steps of the procedure on multiple occasions. Results: Thirty-two residents who participated in the workshops were surveyed, with a 97% response rate (16 of 16 from anesthesiology, 15 of 16 from emergency medicine). Participants commented positively on the workshop's authenticity, its structure, the quality of the feedback provided, and its perceived impact on improving skills in surgical cricothyrotomy. We analyzed narrative comments related to three domains: preparation for the procedure, performing the procedure, and maintaining the skills. Participants highlighted the importance of performing the procedure many times and mentioned the representativeness of fresh cadavers. Discussion: We developed a surgical cricothyrotomy simulation workshop for anesthesiology and emergency medicine residents. Residents in the two specialities uniformly appreciated its format and content. We identified common pitfalls when executing the procedure and provided practical tips and material to facilitate implementation, in particular to face the COVID-19 pandemic.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".