Predictors of difficulty in intubation in patients with obstructive sleep apnoea.
Bibliographic record
Abstract
OBJECTIVE: To evaluate predictors of difficult intubation in patients with obstructive sleep apnoea (OSA). METHODOLOGY: Prospective series of 405 OSA patients (350 males/55 females) who had upper airway surgery. Procedures included functional endoscopic sinus surgery, septoplasty, turbinate reduction, palate/tonsil surgery, and/or tongue base surgery. Intubation difficulty (ID) was assessed using Mallampati grade, Laryngoscopic grade (Cormack and Lehane), and clinical parameters including BMI, neck circumference, thyromental distance, jaw adequacy, neck movements and glidescope grading. RESULTS: Mean age was 41.6 years old; mean BMI 26.6; mean neck circumference 44.5cm; mean Apnea Hypopnea Index (AHI) was 25.0; and mean LSAT 82%. The various laryngeal grades (based on Cormack and Lehane), grade 1 - 53 patients (12.9%), grade 2A - 127 patients (31.0%), grade 2B - 125 patients (30.5%), grade 3 - 93 patients (22.7%) and grade 4 - seven patients (1.7%); hence, 24.4% had difficulties in intubation. Parameters that adversely affected intubation were, age of the patient, opening of mouth, retrognathia, overbite, overjet, limited neck extension, thyromental distance, Mallampati grade, and macroglossia (p<0.001). Body mass index (BMI) (p=0.087), neck circumference (p=0.645), neck aches (p=0.728), jaw aches (p=0.417), tonsil size (p=0.048), and AHI (p=0.047) had poor correlation with intubation. BMI-adjusted for Asians and Caucasians, showed that Asians were more likely to have difficulties in intubation (adjusted OR = 4.6 (95%Confidence Interval: 1.05 to 20.06) (p=0.043), compared to the Caucasian group. CONCLUSION: This study illustrates that difficult intubation can be predicted pre-surgery in order to avert any anaesthetic morbidity.
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 | 0.000 | 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 | 0.001 | 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".