Presence and Predictive Value of Obsessive-Compulsive Symptoms in Anxiety and Depressive Disorders
Bibliographic record
Abstract
OBJECTIVE: Obsessive-compulsive symptoms (OCS) co-occur frequently with anxiety and depressive disorders, but the nature of their relationship and their impact on severity of anxiety and depressive disorders is poorly understood. In a large sample of patients with anxiety and depressive disorders, we assessed the frequency of OCS, defined as a Young Adult Self-Report Scale-obsessive-compulsive symptoms score >7. The associations between OCS and severity of anxiety and/or depressive disorders were examined, and it was investigated whether OCS predict onset, relapse, and persistence of anxiety and depressive disorders. METHODS: Data were obtained from the third (at 2-year follow-up) and fourth wave (at 4-year follow-up) of data collection in the Netherlands Study of Anxiety and Depression cohort, including 469 healthy controls, 909 participants with a remitted disorder, and 747 participants with a current anxiety and/or depressive disorder. RESULTS: OCS were present in 23.6% of the total sample, most notably in those with current combined anxiety and depressive disorders. In patients with a current disorder, OCS were associated with severity of this disorder. Moreover, OCS predicted (1) first onset of anxiety and/or depressive disorders in healthy controls (odds ratio [OR], 5.79; 95% confidence interval [CI], 1.15 to 29.14), (2) relapse in those with remitted anxiety and/or depressive disorders (OR, 2.31; 95% CI, 1.55 to 3.46), and (3) persistence in patients with the combination of current anxiety and depressive disorders (OR, 4.42; 95% CI, 2.54 to 7.70) within the 2-year follow-up period Conclusions: OCS are closely related to both the presence and severity of anxiety and depressive disorders and affect their course trajectories. Hence, OCS might be regarded as a course specifier signaling unfavorable outcomes. This specifier may be useful in clinical care to adapt and intensify treatment in individual patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".