The effect of obsessive compulsive symptoms on psychopathology in patients with schizophrenia
Bibliographic record
Abstract
Abstract Background: There is a growing interest on the impact of comorbid obsessive-compulsive symptoms (OCS) on the course and severity of schizophrenia in recent years. Objective: This study determined the prevalence of OCS in schizophrenia patients and the clinical outcomes of the comorbidity. Methods: A total of 220 schizophrenia patients were recruited. All the participants completed Structure Clinical Interview version, Yale Brown Obsessive Compulsive Scale, Calgary Depression Scale for Schizophrenia, Columbia Suicide Severity Rating Scale and World Health Organization Quality of Life – Brief Version (WHOQOL-BREF). Results: Significantly higher number of schizophrenia patients with OCS were taking Clozapine (p = 0.023) and antidepressants (p = 0.013). Schizophrenia patients with OCS showed more severe positive (p < 0.001) and general symptoms (p < 0.001) of schizophrenia, higher depressive symptoms (p = 0.013), higher suicidality (p < 0.001), more hospitalization (p = 0.044), poorer physical (p = 0.034) and psychological (p = 0.032) domain in WHOQOL-BREF. Discussion: Schizophrenia patients with OCS are associated with more severe psychopathology and depressive symptoms which subsequently suffered poorer physical and psychological health. Hence, recognition of OCS in schizophrenia and early initiation of effective treatment may be able to reduce the burden for people with chronic mental illness.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".