Associations With Severe Desaturation Events Among Children Receiving Noninvasive Respiratory Support at Time of Intubation
Bibliographic record
Abstract
BACKGROUND: Endotracheal intubation is a common procedure associated with adverse events, including severe desaturation. Many patients receive noninvasive respiratory support to reduce the need for intubation. There are minimal data about the association between noninvasive respiratory support and the risk of a severe desaturation event during intubation. We aim to differentiate patients based on the level of noninvasive respiratory support, analyze the severe desaturation event by groups, and identify modifiable risk factors. METHODS: Oral intubations, excluding tube exchanges or re-intubation after unplanned extubation, from October 2018 through July 2020, at the study site were reviewed. A severe desaturation event was defined as [Formula: see text] < 70% or a >15% decrease from baseline in cyanotic heart disease. We analyzed outcomes by 4 groups: room air/nasal cannula (≤0.5 L/kg/min), high-flow nasal cannula (HFNC) (0.5-2 L/kg/min), high HFNC (≥2 L/kg/min), and noninvasive ventilation (NIV). RESULTS: = .02) among those with a severe desaturation event. In a regression analysis, when adjusting for ≥2 intubation attempts pre-intubation, NIV use was independently associated with increased odds of severe desaturation events (odds ratio 3.14, CI 1.08-10.5). CONCLUSIONS: Results of our study suggest that [Formula: see text] > 0.60, the duration of noninvasive respiratory support, and exposure to NIV before an intubation are risk factors of severe desaturation events during intubation.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".