Neurocognitive risk markers in pediatric obsessive–compulsive disorder
Bibliographic record
Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) has complex genetic underpinnings, particularly in its early-onset form, which places siblings at a 10-fold increased risk of developing the disorder. Examination for neurocognitive markers preceding pediatric OCD onset has not been conducted, although markers have been identified in adult OCD. This study compared neurocognition across groups of OCD-affected youth (n = 87), unaffected siblings of those with early-onset OCD (n = 67), and healthy controls (HC; n = 79). METHODS: A total of 233 participants aged 6-18 years old completed standardized neurocognitive tests of cognitive flexibility, decision making, planning, response inhibition, spatial working memory, attention, recognition nonverbal memory, and intelligence. They were administered the Anxiety Disorders Interview Schedule-Parent version (ADIS-P) and completed self-report anxiety and OCD questionnaires. Linear mixed-effects models tested for differences between groups, adjusting for age, gender, IQ, state anxiety, and ethnicity, and accounting for random effects of family membership. RESULTS: OCD-affected youth and unaffected siblings performed significantly worse on planning in comparison to HCs (Cohen's d = 0.74; 95% CI = [0.11, 1.36]; Cohen's d = 0.75; 95% CI = [0.12, 1.38], respectively; omnibus group effect p = .007). No other significant between-group differences were identified. CONCLUSIONS: Neurocognitive performance differences between groups identified planning as a preexisting trait marker of pediatric OCD, while no other domain presented as a marker of pediatric OCD. This differs from adult OCD, which is associated with broader cognitive impairments. Investigating longitudinal trajectories and predictive significance of neurocognition in those affected by, and at risk for, early-onset OCD is warranted. Ideally, this will enhance individualized risk stratification and inform future prevention and early intervention strategies.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".