Comparison of Procedural Sedation Outcomes in Children With and Without Autism Spectrum Disorder
Bibliographic record
Abstract
OBJECTIVE: Children with autism spectrum disorder (ASD) often have comorbidities predisposing them to higher risk for sedation-related adverse events (AEs). Our objective was to compare sedation-related AEs in children with and without ASD. PATIENTS AND METHODS: We conducted a multicenter study of children aged 3-18 years with and without ASD who underwent sedation outside the operating room from May 1, 2022, to April 30, 2023, using the Pediatric Sedation Research Consortium database. The primary exposure was ASD diagnosis. We compared AEs in children with and without ASD after matching for age, sex, procedure, and American Society of Anesthesiologists classification. RESULTS: Of 64 708 children analyzed, 4421 (6.8%) had an ASD diagnosis. A higher proportion of children with ASD were male (75.4% vs 54.7%), obese (6.0% vs 2.9%), and had a comorbid condition (93.5% vs 40.3%). Matched analysis showed no significant difference in critical AEs (odds ratio [OR], 0.63; 95% CI, 0.23-1.49). However, children with ASD had greater odds of high risk (OR, 1.52; 95% CI, 1.26-1.83) and low-risk AEs (OR, 1.39; 95% CI, 1.25-1.55) compared with children without ASD. Specifically, children with ASD had greater odds of hypoxia (OR, 1.31; 95% CI, 1.10-1.56) and complete (OR, 1.89; 95% CI, 1.27-2.81) or partial (OR, 1.43; 95% CI, 1.25-1.63) airway obstruction. CONCLUSIONS: Children with ASD are at a higher risk for sedation-related airway AEs and significant respiratory interventions, although the incidence of critical AEs was low.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".