Ultrasound-measured cutaneous-epiglottic distance for predicting difficult laryngoscopy: an observational study
Bibliographic record
Abstract
BACKGROUND: Ultrasound (US) allows for rapid bedside airway assessment. We aimed to evaluate the US-measured cutaneous-epiglottic distance (CED) in predicting difficult laryngoscopy (Cormack-Lehane grades 3‒4). We also evaluated the potential association between CED, sex, patient's body mass index (BMI), and the independent associations between CED and increased odds for Cormack-Lehane grades 3‒4 (secondary outcomes). METHODS: and/or previous history of difficult intubation were excluded. CED was measured with patients anesthetized before tracheal intubation. Age, sex, BMI, type of surgery, and number of attempts until successful tracheal intubation were recorded. Receiver operator characteristic (ROC) curve analysis was performed to evaluate CED's clinical relevance. Secondary analyses compared the association between CED and BMI in patients with Cormack-Lehane grades 1‒2 versus those with grades 3‒4. The relationship between CED and BMI was assessed using multiple linear regression. Binary logistic regression was employed for predicting Cormack-Lehane grades 3‒4 as a dichotomous outcome with CED and BMI as a covariate. RESULTS: ROC curve analysis revealed an area under the curve of 0.899 (p < 0.001). The maximum CED cut-off point (by Youden index) was 25.6 mm. CED and BMI were positively correlated, and both were independently associated with an increased odds for difficult laryngoscopy [odds ratio for CED = 1.81, 95% confidence interval (CI) 1.35‒2.41; BMI = 1.30, 95% CI 1.05‒1.59]. CONCLUSION: US-measured CED has a high discriminatory capability for predicting lower (1‒2) and higher (3‒4) Cormack-Lehane grades during direct laryngoscopy. CED was positively correlated with BMI and was independently associated with higher odds for difficult laryngoscopy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".