Avoidance, Insight, Impairment Recognition Concordance, and Cognitive-Behavioral Therapy Outcomes in Pediatric Obsessive-Compulsive Disorder
Bibliographic record
Abstract
OBJECTIVE: Insight and avoidance are commonly discussed factors in obsessive-compulsive disorder (OCD) that have demonstrated associations with increased severity as well as reduced treatment response in adults, but these factors have not been sufficiently examined in pediatric OCD. This study examined the impacts of avoidance, insight, and impairment recognition concordance on cognitive-behavioral therapy (CBT) outcomes as well as impacts of CBT on insight and avoidance in a large sample of youths affected by OCD. METHOD: Data from 573 OCD-affected youths enrolled in CBT trials were aggregated. Children's Yale-Brown Obsessive-Compulsive Scale items measured treatment response, insight, and avoidance. Standardized differences between child and parent ratings of impairment were used to calculate impairment recognition concordance. Binary logistic regression was used to identify variables associated with treatment response. RESULTS: Greater avoidance, limited child recognition of impairment, older age, and lower baseline severity predicted reduced likelihood of treatment response, but insight did not. Both insight and avoidance improved significantly following CBT. Response rates were lower when posttreatment insight and avoidance were worse. CONCLUSION: Contrasting with prevailing belief, poor insight does not appear to limit CBT response potential in pediatric OCD. Avoidance and impairment recognition are understudied CBT response predictors and warrant further consideration in pediatric OCD. Clinicians should attend to these factors to optimize outcomes for children affected by this common, debilitating illness.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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 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".