The cuff leak test in critically ill patients: An international survey of intensivists
Bibliographic record
Abstract
BACKGROUND: The cuff leak test (CLT) is used to assess laryngeal edema prior to extubation. There is limited evidence for its diagnostic accuracy and conflicting guidelines surrounding its use in critically ill patients who do not have risk factors for laryngeal edema. The primary study aim was to describe intensivists' beliefs, attitudes, and practice regarding the use of the CLT. METHODS: A 13-item survey was developed, pilot-tested, and subjected to clinical sensibility testing. The survey was distributed electronically through MetaClinician®. Descriptive statistics and multivariable regression analysis were performed to examine associations between participant demographics and survey responses. RESULTS: 1184 practicing intensivists from 17 countries in North and South America, Europe, Oceania, and Asia participated. The majority (59%) of respondents reported rarely or never perform the CLT prior to extubating patients not at high risk of laryngeal edema, which correlated with 54% of respondents reporting they believed a failed CLT did not predict reintubation. Intensivists from the Middle East were 2.4 times more likely to request a CLT compared to those from North America. Intensivists with base training in medicine or emergency medicine were more likely to request a CLT prior to extubation compared to those with base training in anesthesiology. CONCLUSION: Use of the CLT prior to extubating patients not at high risk of laryngeal edema in the intensive care unit is highly variable. Practice appears to be influenced by country of practice and base specialty training.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".