Incidence of difficult bag‐mask ventilation in children: a prospective observational study
Bibliographic record
Abstract
BACKGROUND: Difficult airway (DA), including difficult bag-mask ventilation (DBMV), and difficult intubation (DI) is an important challenge for the pediatric anesthesiologist. While expected DBMV can be successfully managed with appropriate equipment and personnel, unexpected DBMV relies on the resources available and the experience of the anesthesiologist at the time of the emergency. The incidence and risk factors of unexpected DA in otherwise healthy children, including DBMV among pediatric patients are not known. The aim of this study was to expand the scientific knowledge of unexpected DBMV among pediatric patients. METHODS: Patients between the ages of 0 and 8 years, undergoing elective surgery requiring bag-mask ventilation BMV and intubation at the Montreal Children's Hospital were recruited in this prospective observational study. Data on the incidence of DBMV and risk factors were collected over a 3-year period. RESULTS: In a sample of 484 children, the incidence of unexpected difficult BMV was 6.6% (95% CI [4.6, 9.2]). The incidence of expected DA among the screened patients (N = 4865) was 0.5% (95% CI [0.3, 0.7]). In a logistic regression analysis, age (OR 0.98; 95%CI [0.97, 0.99]), undergoing otolaryngology (ENT) surgery (OR 2.92; 95% CI [1.08, 7.95]) and use of neuromuscular blocking agents (OR 3.49; 95%CI [1.50-8.11]) were independently associated with DBMV. The incidence of DI was 1.2%. No association between DBMV and DI was found (Fisher's exact test, P = 1.0). CONCLUSIONS: This is the first published report of the incidence of unexpected DBMV among healthy pediatric patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".