Cannot Intubate???Cannot Ventilate and Difficult Intubation Strategies: Results of a Canadian National Survey
Bibliographic record
Abstract
In Brief The purpose of this study was to determine the preferences of Canadian anesthesiologists in difficult intubation and cannot intubate–cannot ventilate (CICV) situations. Using a mailed survey, we asked anesthesiologists their preferences for and comfort level in using (a) alternative airway devices in a difficult intubation scenario and (b) infraglottic airway in a CICV scenario. Chi-square analysis and Student’s t-test were used for categorical and continuous variables. Nine-hundred-seventy-one of 2066 surveys were returned. In the difficult intubation scenario, the preferred alternative airway devices were lighted stylet (45%), fiberoptic bronchoscope (26%), and intubating laryngeal mask airway (20%). Only 57% of respondents had encountered a CICV situation in real life. In the CICV scenario, preferred infraglottic airways were cricothyroidotomy by IV catheter (51%), percutaneous cricothyroidotomy (28%), and tracheostomy by surgeon (14%). Anesthesiologists had little experience and were uncomfortable with open surgical infraglottic airways. Anesthesiologists with experience using infraglottic airways on mannequins were more comfortable using them in patients (P < 0.001). In conclusion, in a difficult intubation scenario, the lighted stylet has emerged as the preferred alternative airway device. In a CICV scenario, respondents preferred cricothyroidotomy by IV catheter, followed by percutaneous cricothyroidotomy and tracheostomy by surgeon. Practice on mannequins was associated with improved comfort in using infraglottic airways in patients. IMPLICATIONS: Our survey showed that cricothyroidotomy by IV catheter and percutaneous cricothyroidotomy were the preferred infraglottic airways in a cannot-intubate-cannot-ventilate scenario. Respondents were comfortable with these two techniques but not with an open surgical airway. Practice on mannequins was associated with improved comfort in using infraglottic airways in patients.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".