Neck circumference as a predictor of difficult intubation and difficult mask ventilation in morbidly obese patients
Bibliographic record
Abstract
BACKGROUND: There is conflicting evidence as to whether obesity and neck circumference are predictors of difficult intubation in the surgical population. In addition, the cut-off neck circumference related to difficult intubation has not been clearly identified. OBJECTIVES: The primary study objective was to determine whether neck circumference and obesity were predictors of difficult intubation in morbidly obese surgical patients. Secondary outcomes included difficult mask ventilation. DESIGN: A prospective, noninterventional study. SETTING: Canadian tertiary care surgical centre between October 2012 and August 2013. PATIENTS: A total of 104 morbidly obese surgical patients (BMI ≥40 kg m(-2)) were included in the study. Eighty-eight patients were women and 16 were men. Exclusions were known difficult airway and emergency surgery. MAIN OUTCOME MEASURES: The primary outcome of the study was difficult tracheal intubation. An Intubation Difficulty Scale (IDS) was derived using seven parameters and difficult intubation was defined as IDS of at least 5. The secondary outcome was difficult mask ventilation; mask ventilation was graded as easy or difficult (inadequate, desaturation, two-handed or impossible). RESULTS: Univariate analyses showed that difficult intubation was associated with neck circumference, males, BMI more than 50 kg m(-2), American Society of Anesthesiologists (ASA) status and waist circumference, and difficult mask ventilation with neck circumference, males, BMI more than 50 kg m(-2) and thyromental distance. Multiple logistic regression analysis showed that neck circumference more than 42 cm (P = 0.044) and BMI more than 50 kg m(-2) (P = 0.017) were independent predictors of difficult intubation. Male sex (P = 0.004) and BMI more than 50 kg m(-2) (P = 0.031) were independent predictors of difficult mask ventilation.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".