Impact of Body Mass Index on Adverse Events in Children Undergoing Sedation for MRI
Bibliographic record
Abstract
OBJECTIVES: More than 20% of children in the United States have a nonhealthy body mass index (BMI). Magnetic resonance imaging is a common procedure necessitating sedation in children, including those with nonhealthy BMI values. We aimed to determine the risk of adverse events (AEs) and airway interventions associated with BMI in this population. METHODS: A retrospective cross-sectional study of children undergoing sedation for MRI at 66 centers participating in the Pediatric Sedation Research Consortium was conducted. BMI values were categorized as underweight, healthy weight, overweight, obesity, and severe obesity. Outcomes were AEs and airway interventions. AEs were categorized as major, moderate, or minor, and airway interventions were categorized as major or minor. We used multivariable logistic regression to determine the risk of AEs and airway interventions associated with BMI. RESULTS: We analyzed 39 393 children; 3132 (8%) were underweight, 5476 (13.9%) were overweight, 4446 (11.2%) had obesity, and 1123 (2.9%) had severe obesity. Risk of major and moderate AEs in children categorized as underweight, overweight, obesity, and severe obesity were adjusted odds ratio (aOR) 1.22 (95% CI: 1.04-1.4), 1.58 (95% CI 1.41-1.74), 1.8 (95% CI 1.55-2.04), and 1.86 (95% CI 1.59-2.13), respectively. Risk of major airway interventions in those with BMI values in the overweight, obesity, and severe obesity categories were aOR 1.61 (95% CI 1.44-1.78), 1.83 (95% CI 1.58-2.09), and 1.9 (95% CI 1.62-2.18). CONCLUSIONS: Children undergoing sedation for MRI categorized as overweight, obesity, and severe obesity are at increased odds of AEs and airway interventions. Children who are underweight have increased odds of AEs.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".