Prevalence and Correlates of Cognitive Impairment in schizophrenia: A cross-sectional study from Southern Sri Lanka
Bibliographic record
Abstract
Abstract Introductions: Cognitive impairment is a core feature of schizophrenia. This study assessed the prevalence of cognitive impairment, the degree of impairment in individual cognitive domains and sociodemographic and clinical correlates among patients with schizophrenia in southern Sri Lankan. Methods: A cross-sectional study was carried out at the psychiatry outpatient clinics of Teaching Hospital, Karapitiya, Sri Lanka. Their cognitive functions were assessed using the culturally validated Sinhala version of Addenbrooke’s Cognitive Examination – III (ACE-III-S). ACE-III-S score below 85.5 was considered as significant cognitive impairment. Linear and multiple regression analyses were used to assess the factors associated with cognitive impairment. A P value of 0.05 is considered significant. Results: One hundred forty patients with schizophrenia were assessed. Of this, 125 patients had significant cognitive impairment with a prevalence of 89.3% (95% CI:84.1-94.5). Impairment in each cognitive domain was as follows: 60% in attention, 65.7% in memory, 55% in fluency, 61.4% in language, and 63.6% in visuospatial skills. Impairment was not different between cognitive domains. A shorter duration of formal education (P = <0.001), longer duration of illness (P = <0.001) and higher dose of antipsychotics (P = 0.034) were independently associated with cognitive impairment. Conclusions: Nine out of ten patients with schizophrenia experienced significant cognitive impairment. Patients showed more than 50% impairment in all the cognitive domains. The cognitive domains did not show disproportionate impairment. This study highlights the importance of introducing routine cognitive assessment protocols and frequent reviews of antipsychotic medications.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".