Airway Assessment in Patients Undergoing Surgery and General Anaesthesia and its Application in Prediction of Difficult Airway
Bibliographic record
Abstract
Background and aim: Anaesthesiologists face challenges in airway management, but pre-anesthetic airway assessment helps identify potential complications and prepare alternative plans for children with challenging airways. The primary goal of airway assessment during pre-anesthetic evaluation is to identify difficult airways and prepare alternative treatments for mask ventilation, direct laryngoscopy, and endotracheal intubation. Material and methods: Patients aged 8-14 years, of either sex, with an American Society of Anesthesiologists (ASA) grade I or II, were included. Four parameters, namely the modified Mallampati test, neck circumference, measurement of thyromental distance, and the Ratio of height to thyromental distance, were assessed pre-operatively using the same flexible measuring tape to avoid instrumental bias. These parameters were then correlated with Cormack and Lehane's grading system for assessing a difficult airway. Results: All four parameters, namely modified Mallampati test, thyromental distance, Ratio of height to thyromental distance, and neck circumference, were found to be statistically significant in predicting difficult airway in children of the age group 8-14 years. Out of the four parameters assessed, the Ratio of Height to Thyromental Distance (RHTMD) had the highest sensitivity of 98.41%, followed by Mallampati grading (MPG) with a sensitivity of 96.83%, suggesting that they are highly sensitive predictors of difficult airways in children. Conclusions: To predict airway status in children, the modified Mallampati test is the most useful parameter, which can be used as a bedside screening test in the 8-14 year age group, as it has high sensitivity and the highest diagnostic accuracy.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".