Decision: Characterizing the clinical and sociodemographic profiles of hospitalized adolescents with autism spectrum disorder — R1/PR6
Bibliographic record
Abstract
The prevalence of autism spectrum disorder (ASD) is increasing worldwide. Youngsters with ASD demonstrate higher rates of intellectual disabilities (IDs), comorbid psychopathology and psychiatric hospitalizations, compared to children in the general population. This study characterizes the demographics and clinical parameters of adolescent psychiatric inpatients with ASD compared to inpatients without ASD, all hospitalized during the study period. Additionally, within the ASD group, those with ID were compared to those without. The rate of males among participants with ASD was significantly higher than among those without ASD, and the duration of hospitalization was longer. In contrast, the rate of cigarette smoking, major depressive disorder and suicidal thoughts among those with ASD was lower. One-third of those with ASD had moderate to severe ID, about 10% had comorbid epilepsy, and about half of them demonstrated aggressive behavior. Most ASD patients showed significant improvement upon discharge, although the extent of improvement was more prominent among ASD patients with no ID. Our findings, consistent with previous research, indicate that hospitalization is beneficial to youths with ASD, both those with and those without ID. Further studies that include long-term follow-up are needed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".