Exploring latent clusters in pediatric OCD based on symptoms, severity, age, gender, and comorbidity
Bibliographic record
Abstract
Given diverse symptom expression and high rates of comorbid conditions, the present study explored underlying commonalities among OCD-affected children and adolescents to better conceptualize disorder presentation and associated features. Data from 830 OCD-affected participants presenting to OCD specialty centers was aggregated. Dependent mixture modeling was used to examine latent clusters based on their age- and gender adjusted symptom severity (as measured by the Children's Yale-Brown Obsessive-Compulsive Scale; CY-BOCS), symptom type (as measured by factor scores calculated from the CY-BOCS symptom checklist), and comorbid diagnoses (as assessed via diagnostic interviews). Fit statistics favored a four-cluster model with groups distinguished primarily by symptom expression and comorbidity type. Fit indices for 3-7 cluster models were only marginally different and characteristics of the clusters remained largely stable between solutions with small clusters of distinct presentations added in more complex models. Rather than identifying a single classification system, the findings support the utility of integrating dimensional, developmental, and transdiagnostic information in the conceptualization of OCD-affected children and adolescents. Identified clusters point to the centrality of contamination concerns to OCD, relationships between broader symptom expression and higher levels of comorbidity, and the potential for complex/neurodevelopmental presentations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".