Front-of-neck airway meets front-of-neck simulation: improving cricothyroidotomy skills using a novel open-access three-dimensional model and the Airway App
Bibliographic record
Abstract
To the Editor,Although anesthesiologists, intensivists, and emergency physicians rarely perform cricothyroidotomy, these specialists must maintain the knowledge, decisionmaking, and procedural skill sets to perform cricothyroidotomy quickly and safely.The 2015 Difficult Airway Society guidelines recommend bougie-assisted scalpel cricothyrotomy coupled with regular simulated practice.1 Regardless of the technique used, the training must be realistic, addressing both technical and non-technical skills and facilitating reflection and data gathering.We outline a novel approach to cricothyrotomy training that we introduced at a recent airway workshop.A We are eager to share it, receive feedback, and further develop this airway education and patient safety initiative.Previously, as in many centres, our airway workshops focused on the acquisition of manual skills using mannequins and isolated pig tracheas.As the Canadian Airway Focus Group emphasizes, however, manual skills are only one aspect required for reliable patient rescue in a ''cannot intubate/cannot oxygenate'' emergency.2 Specifically, we wished to address the oft-cited
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.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".