Factors that impact second attempt success for neonatal intubation following first attempt failure: a report from the National Emergency Airway Registry for Neonates
Bibliographic record
Abstract
OBJECTIVE: To determine the factors associated with second attempt success and the risk of adverse events following a failed first attempt at neonatal tracheal intubation. DESIGN: Retrospective analysis of prospectively collected data on intubations performed in the neonatal intensive care unit (NICU) and delivery room from the National Emergency Airway Registry for Neonates (NEAR4NEOS). SETTING: Eighteen academic NICUs in NEAR4NEOS. PATIENTS: Neonates requiring two or more attempts at intubation between October 2014 and December 2021. MAIN OUTCOME MEASURES: The primary outcome was successful intubation on the second attempt, with severe tracheal intubation-associated events (TIAEs) or severe desaturation (≥20% decline in oxygen saturation) being secondary outcomes. Multivariate regression examined the associations between these outcomes and patient characteristics and changes in intubation practice. RESULTS: 5805 of 13 126 (44%) encounters required two or more intubation attempts, with 3156 (54%) successful on the second attempt. Second attempt success was more likely with changes in any of the following: intubator (OR 1.80, 95% CI 1.56 to 2.07), stylet use (OR 1.65, 95% CI 1.36 to 2.01) or endotracheal tube (ETT) size (OR 2.11, 95% CI 1.74 to 2.56). Changes in stylet use were associated with a reduced chance of severe desaturation (OR 0.74, 95% CI 0.61 to 0.90), but changes in intubator, laryngoscope type or ETT size were not; no changes in intubator or equipment were associated with severe TIAEs. CONCLUSIONS: Successful neonatal intubation on a second attempt was more likely with a change in intubator, stylet use or ETT size.
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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 | 0.011 |
| 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 | 0.001 |
| 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".