Sublingual ultrasound as an assessment method for predicting difficult intubation: a pilot study
Bibliographic record
Abstract
Current methods to assess the airway before tracheal intubation are variable in their ability to predict a difficult airway accurately. We hypothesised that sublingual ultrasound could provide additional information to predict a difficult airway with greater success than current methods. We recruited 110 patients to perform sublingual ultrasound on themselves following brief instruction. Ability to view the hyoid bone on sublingual ultrasound, mouth opening distance, thyromental distance, neck mobility, size of mandible and modified Mallampati classification were recorded and assessed for ability to predict a difficult intubation based on the grade of laryngoscope. Visibility of the hyoid using ultrasound was associated with a laryngoscopic grade of 1-2 (p < 0.0001), and (p < 0.0001) had a positive likelihood ratio of 21.6 and a negative likelihood ratio of 0.28. Each of the other methods had considerably lower positive likelihood ratios and lower sensitivity. Our results suggest that sublingual ultrasound is a potential tool for predicting a difficult airway in addition to conventional methods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".