Cluster Analysis of Symptom Types, Severity, Age, Gender, and Comorbidity in Pediatric Obsessive-Compulsive Disorder
Bibliographic record
Abstract
Background: Obsessive-compulsive disorder (OCD) manifests in various ways and often co-occurs with other conditions, affecting about 70% of patients. This study aims to explore the underlying commonalities among OCD-affected children and adolescents in order to better conceptualize variations in disorder presentation.<br/>Methods: Data from seven international programs focusing on pediatric OCD were pooled, comprising 830 cases aged 5-19, with 54% being female. The severity and types of OCD symptoms were assessed using the Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS), while comorbid conditions were determined through diagnostic interviews. Dependent mixture modeling was employed to identify latent groups based on age, gender, symptom severity, type, and comorbidities.<br/>Results: The modeling revealed four distinct clusters, primarily differentiated by symptom expression and comorbidity types. While fit indices for 3-7 clusters showed minimal variance, cluster characteristics remained largely consistent across different models, with additional smaller clusters in more complex models.<br/>Conclusions: Integrating dimensional, developmental, and transdiagnostic information proved valuable in understanding OCD in children and adolescents. The identified clusters underscored the significance of contamination symptoms, associations between broader symptomatology and increased comorbidity, and the potential for intricate neurodevelopmental profiles. These clusters offer insights into potential adaptations for treatment approaches.<br/>
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.003 | 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 teacher head, 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".