Obsessive‐compulsive symptoms in schizophrenia: Prevalance and clinical correlates
Bibliographic record
Abstract
Obsessive-compulsive symptoms (OCS) have been observed in a substantial proportion of schizophrenic patients. In this study, the rate of occurrence of OCS and obsessive-compulsive disorder (OCD) in schizophrenic patients, and also the interrelationship between OCS and schizophrenic symptoms and depressive symptoms were assessed. A total of 100 subjects with a diagnosis of schizophrenia from the 4th edition of the Diagnostic and Statistical Manual (DSM-IV) were evaluated by the structured and clinical interview for axis-1 DSM-IV disorders-patient edition (SCID-P), the Positive and Negative Syndrome Scale (PANSS), Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), and the Calgary Depression Rating Scale for Schizophrenia. The prevalance of OCS in individuals meeting criteria for schizophrenia was 64%. A total of 30 of these patients (Y-BOCS total score > or =7) also met the DSM-IV criteria for OCD. The total score on Y-BOCS was significantly correlated with total score on PANSS, Positive-PANSS score, General-PANSS score and total score on Calgary Depression Rating Scale for Schizophrenia. OCS and OCD relatively frequent in schizophrenic patients and OCS are significantly correlated with the severity of psychosis, positive symptoms, and depressive symptoms in schizophrenic patients. These findings provide further evidence for the importance of OCS in schizophrenia.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".