Sex Differences in Psychiatric Comorbidities in Adolescents With Autism Spectrum Disorder
Bibliographic record
Abstract
Objective: To investigate sex differences in psychiatric comorbidities in adolescents with autism spectrum disorder (ASD). Methods: The US National Inpatient Sample dataset (January 2016 to December 2018) was used for this retrospective study. The patient population was selected by performing a query on all adolescent patients (aged 12–17 years) having ASD with the ICD-10-CM code starting with F84. All missing sex data were excluded. Additional data on mood disorders, anxiety disorders, personality disorders, adjustment disorders, psychotic disorders, attention-deficit/hyperactivity disorder (ADHD)/conduct disorders, sleep-wake disorders, and substance use disorders were collected. Data on psychiatric comorbidities were collected using the ICD-10-CM code provided in the Clinical Classifications Software of the dataset. Results: Mood disorders (37.4% vs 44.1%, P < .001) and anxiety disorders (29.4% vs 37.0%, P < .001) were more prevalent in females compared to males. The prevalence of ADHD and other conduct disorders was significantly higher in males than females (47.7% vs 36.7%, P < .001). Substance use disorders were slightly higher among males compared to females (3.7% vs 3.0%, P = .04). Conclusion: The study findings revealed statistically significant disparities in psychiatric comorbidities among adolescent male and female patients with ASD. These findings could serve as a pilot for larger-scale research with this patient population in the future.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".