Bibliographic record
Abstract
The aim of this study was to evaluate the prevalence of comorbid obsessive compulsive symptoms/disorder and its impact on outcome among patients with schizophrenia. 181 patients with schizophrenia were evaluated on Yale-Brown Obsessive–Compulsive Symptom Checklist, Yale-Brown Obsessive–Compulsive Scale, Calgary Depression Scale for Schizophrenia, Positive and Negative Symptom Scale, Social Occupational Functioning Scale, Global Assessment of Functioning Scale and Indian Disability Evaluation and Assessment Scale. Slightly more than one-fourth of patients fulfilled the diagnosis of current (28.2%) and lifetime (29.8%) diagnosis of obsessive compulsive disorder. On Yale Brown Obsessive Compulsive Symptom Checklist, the most common lifetime obsessions were those of contamination (25.4%), followed by obsessions of need for symmetry or exactness (11.6%). The most common compulsions were those of cleaning/washing (27.1%), followed by those of checking (24.3%). Presence of obsessive compulsive symptoms was associated with younger age of onset, higher prevalence of comorbid depression, and current suicidal ideations. Thus, it can be concluded that a significant proportion of patients with schizophrenia have obsessive compulsive symptoms/disorder. Clinicians managing patients of schizophrenia should evaluate the patients thoroughly for presence of comorbid obsessive compulsive symptoms/disorder and must take the same into account while managing the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".