Sex Differences in Psychiatric Comorbidities in Adolescents With Autism Spectrum Disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it