Difficult and failed intubation: Incident rates and maternal, obstetrical, and anesthetic predictors Intubation difficile et echec de l'intubation: incidence et predicteurs maternels, obstetricaux et anesthesiques
Bibliographic record
Abstract
Background Difficult and failed tracheal intubation may be more common in the obstetrical population. The objective of this study was to determine the incidence of difficult and failed tracheal intubation in a Canadian tertiary care obstetric hospital and to identify predictors. Methods Maternal, perinatal, and anesthetic information on all pregnant women or recently pregnant (up to three days postpartum) women undergoing general anesthesia (GA) from 1984 to 2003 at the Izaac Walton Killam Health Centre (IWK) was abstracted from the Nova Scotia Atlee Perinatal Database, and the information was augmented by chart review. The incidence and predictors of difficult and failed tracheal intubation were determined. Analyses using logistic regression were performed for the complete GA cohort and for the subgroup that had Cesarean delivery under GA. Results There were 102,587 deliveries of C20 weeks gestation in the study population, with 3,107 GAs identified, 2,986 records reviewed, and 2,633 GAs (88%) retained in the complete cohort. Difficult tracheal intubation was encountered in 123 of 2,633 (4.7%) women in the complete cohort and 60 of 1,052 (5.7%) women in the Cesarean delivery subgroup. Only two failed tracheal intubations were identified (0.08%) in the complete cohort, and both occurred during GAs for postpartum tubal ligation. The combined rate of difficult/failed tracheal
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".