Child and Adolescent OCD Symptom Patterns: A Factor Analytic Study
Bibliographic record
Abstract
Objectives: To investigate OCD symptom structure in children and adolescents in order to identify OCD subtypes. A few studies have tried to identify symptom based subtypes of OCD in Children and Adolescents, and found it to contain 4-5 factors. There are several reasons for the different number of factors found such as different methods and limited sample sizes. Therefore, this study based on a large sample will be a valuable addition to the previous studies. Methods: Exploratory factor analysis will be applied to the Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) symptom checklist items in order to reveal any latent factor structure. The relation between specific factors and co-morbid disorders will be examined. Data for 696 children and adolescents with OCD is already collected and the final sample is expected to be around 850 subjects, collected from 24 different research units in Europe and USA. This study is a part of the Nordic Long-Term OCD Treatment Study (NordLOTS). Results: Work in progress. Preliminary results will be available at the time of presentation. Discussion: Our study is unique in that it includes individual CY-BOCS checklist items and it is thus possible that our results will refine or go beyond the “classical” factors in the original publication. The importance of our findings for studies on OCD genetics and pathogenesis of symptoms will be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".