A prospective observational evaluation of an anatomically guided, logically formulated airway measure to predict difficult laryngoscopy
Bibliographic record
Abstract
CONTEXT: Difficulty during tracheal intubation is the most common cause of serious adverse respiratory events for patients undergoing anaesthesia. Current traditional bedside predictors of difficult laryngoscopy have poor sensitivity. A simple method to accurately predict difficult laryngoscopy could greatly improve patient safety. OBJECTIVES: This study examined a novel bedside predictor of difficult laryngoscopy that calculates a ratio of measurements directly affecting the ability to achieve the necessary line of vision (NLV) from the larynx to the operator (NLV ratio). DESIGN: This was a prospective observational study. SETTING: A single tertiary care surgical centre. PATIENTS: We enrolled 2046 patients scheduled for elective surgery under general anaesthesia with anticipated tracheal intubation. INTERVENTION: Prior to surgery, patients had their NLV ratio and standard airway measures recorded. The anaesthesiologist who performed the intubation was blind to the airway assessment and recorded the best view of the larynx according to the Cormack and Lehane scale. Difficult laryngoscopy was defined as a grade 3 or 4 view. MAIN OUTCOME MEASURE: The main outcome measure was the sensitivity and specificity of the NLV ratio measurement for predicting difficult laryngoscopy. RESULTS: Receiver operating characteristics curve analysis of the NLV ratio revealed an optimal sensitivity of only 41% and specificity of 77%. CONCLUSION: Although our novel measurement performed similarly to traditional bedside predictors of difficult laryngoscopy, the sensitivity was too low for the test to be clinically useful. Numerous factors which may be very difficult to predict at the bedside probably contributed to the poor performance of this novel measurement.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".