Associations of Stylet Use during Neonatal Intubation with Intubation Success, Adverse Events, and Severe Desaturation: A Report from NEAR4NEOS
Bibliographic record
Abstract
INTRODUCTION: Intubations are frequently performed procedures in neonatal intensive care units (NICU) and delivery rooms (DR). Unsuccessful first attempts are common as are tracheal intubation-associated events (TIAEs) and severe desaturations. Stylets are often used during intubation, but their association with intubation outcomes is unclear. OBJECTIVE: To compare intubation success, rate of relevant TIAEs, and severe desaturations in neonates intubated with and without stylets. METHODS: Tracheal intubations of neonates in the NICU or DR from 16 centers between October 2014 and December 2018, performed by neonatology or pediatric providers, were collected from the NEAR4NEOs international registry. Primary oral intubations with a laryngoscope were included in the analysis. First-attempt success, the occurrence of relevant TIAEs, and severe oxygen desaturation (≥20% saturation drop from baseline) were compared between intubations performed with versus without a stylet. Logistic regression with generalized estimate equations was used to control for covariates and clustering by sites. RESULTS: Out of 5,292 primary oral intubations, 3,877 (73%) utilized stylets. Stylet use varied considerably across the centers with a range between 0.5 and 100%. Stylet use was not associated with first-attempt intubation success, esophageal intubation, mainstem intubation, or severe desaturations after controlling for confounders. Patient size was associated with these outcomes and much more predictive of success. CONCLUSIONS: Stylet use during neonatal intubation was not associated with higher first-attempt intubation success, fewer relevant TIAEs, or less severe desaturations. These data suggest that stylets can be used based on individual preference, but stylet use may not be associated with better intubation outcomes.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".