Predicting Difficult Intubation in Emergency Department by Intubation Assessment Score
Bibliographic record
Abstract
BACKGROUND: The difficult intubation is associated with failure of emergency tracheal intubation. This study aimed to develop and validate a model for predicting difficult intubation in emergency department (ED). METHODS: A cross-sectional study was conducted in the ED. We collected data from all consecutive adult patients who underwent emergency tracheal intubation. Patients were excluded if they were intubated by low experience intubator. The difficult intubation was defined by grade III or IV of Cormack and Lehane classification. We used multivariable regression model to identify significant predictors of difficult intubation and weighted points proportional to the beta coefficient values. The ability to discriminate was quantified by using the area under receiver operating characteristics curve (AuROC). The bootstrapping method was used to validate the performance. RESULTS: A total of 1,212 intubations were analyzed. One hundred and fifty-seven intubations were enrolled in difficult intubation group. Five independence predictors were identified, and each was assigned a number of points proportional to its beta coefficient: male gender (one), large tongue (two), limit mouth opening (two), poor neck mobility (two), and presence of obstructed airway (three). Intubation assessment score model was created and applied to all subjects. The AuROC was 0.81 (95% confidence interval (CI): 0.77 - 0.85) for the development dataset, and 0.80 (95% CI: 0.76 - 0.85) for the validation dataset. We defined three risk groups: low risk (zero to one points), intermediate risk (two to three points), and high risk (above three points), and the difficult intubation rate was 4.7%, 22.5%, and 53.6%, respectively. CONCLUSIONS: Intubation assessment score model was constructed from patients' simple characteristics and performed well in predicting difficult intubation and can discriminate between with and without difficult intubation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".