Age of onset in obsessive–compulsive disorder: admixture analysis with a large sample
Bibliographic record
Abstract
BACKGROUND: Research into age of onset in obsessive-compulsive disorder (OCD) has indicated significant differences between patients with early and late onset of the disorder. However, multiple criteria have been used arbitrarily for differentiating between early- and late-onset OCD, rendering inconsistent results that are difficult to interpret. METHOD: In the current study, admixture analysis was conducted in a sample of 377 OC patients to determine the number of underlying populations of age of onset and associated demographic and clinical characteristics. Various measures of anxiety, depression, co-morbidity, autism, OCD, tics and attention deficit hyperactivity disorder (ADHD) symptoms were administered. RESULTS: A bimodal age of onset was established and the best-fitting cut-off score between early and late age of onset was 20 years (early age of onset ≤19 years). Patients with early age of onset were more likely to be single. Early age of onset patients demonstrated higher levels of OCD severity and increased symptoms on all OCD dimensions along with increased ADHD symptoms and higher rates of bipolar disorder. CONCLUSIONS: It is suggested that 20 years is the recommended cut-off age for the determination of early versus late age of onset in OCD. Early age of onset is associated with a generally graver OCD clinical picture and increased ADHD symptoms and bipolar disorder rates, which may be related to greater functional implications of the disorder. We propose that age of onset could be an important marker for the subtyping of 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".