Endotracheal Tube Size Adjustments Within Seven Days of Neonatal Intubation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Neonatal endotracheal tube (ETT) size recommendations are based on limited evidence. We sought to determine data-driven weight-based ETT sizes for infants undergoing tracheal intubation and to compare these with Neonatal Resuscitation Program (NRP) recommendations. METHODS: Retrospective multicenter cohort study from an international airway registry. We evaluated ETT size changes (downsizing to a smaller ETT during the procedure or upsizing to a larger ETT within 7 days) and risk of procedural adverse outcomes associated with first-attempt ETT size selection when stratifying the cohort into 200 g subgroups. RESULTS: Of 7293 intubations assessed, the initial ETT was downsized in 5.0% of encounters and upsized within 7 days in 1.5%. ETT downsizing was most common when NRP-recommended sizes were attempted in the following weight subgroups: 1000 to 1199 g with a 3.0 mm (12.6%) and 2000 to 2199 g with a 3.5 mm (17.1%). For infants in these 2 weight subgroups, selection of ETTs 0.5 mm smaller than NRP recommendations was independently associated with lower odds of adverse outcomes compared with NRP-recommended sizes. Among infants weighing 1000 to 1199 g: any tracheal intubation associated event, 20.8% with 2.5 mm versus 21.9% with 3.0 mm (adjusted OR [aOR] 0.62, 95% confidence interval [CI] 0.41-0.94); severe oxygen desaturation, 35.2% with 2.5 mm vs 52.9% with 3.0 mm (aOR 0.53, 95% CI 0.38-0.75). Among infants weighing 2000 to 2199 g: severe oxygen desaturation, 41% with 3.0 mm versus 56% with 3.5mm (aOR 0.55, 95% CI 0.34-0.89). CONCLUSIONS: For infants weighing 1000 to 1199 g and 2000 to 2199 g, the recommended ETT size was frequently downsized during the procedure, whereas 0.5 mm smaller ETT sizes were associated with fewer adverse events and were rarely upsized.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".