Comorbidity in Juvenile Obsessive—Compulsive Disorder: A Report from India
Bibliographic record
Abstract
OBJECTIVE: Using minimal exclusion criteria, to assess systematically the psychiatric comorbidity in children and adolescents with obsessive-compulsive disorder (OCD) and compare the findings with those of previous studies. METHOD: Fifty-four children and adolescents who satisfied DSM-III-R criteria for OCD were assessed using a structured interview schedule, the Children's version of the Yale-Brown Obsessive Compulsive Scale (CY-BOCS), and the questionnaire for tic disorders. All 54 subjects were recruited from the Child and Adolescent Psychiatry (CAP) services of the National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, South India. Diagnoses were determined consensually after a review of all the available data. RESULTS: Comorbidity was found in 69% of the sample: 22% were diagnosed with disruptive disorders; 20% met criteria for mood disorders; 19% had anxiety disorders; and 17% had tic disorders. Only 1 subject had bipolar disorder, and none had psychosis. The rates for individual diagnoses--in particular, the rates for disruptive disorders, bipolar disorder, and psychosis--were considerably lower than those reported in previous studies. CONCLUSIONS: Patterns of comorbidity in this study differed from those previously reported. Novel patterns of comorbidity with disruptive disorders, bipolar disorder, and psychosis reported in a few recent studies were not replicated in this study. These differences are probably due to different ascertainment methods. Comorbidity needs to be assessed in large epidemiological samples before definite associations can be made between certain comorbid disorders and juvenile OCD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".