Using MoCA-Thai to evaluate cognitive impairment in patients with schizophrenia.
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is a chronic devastating illness with specific effects on cognitive function. A few studies have been performed on Asian patients. OBJECTIVE: To examine prevalence of cognitive impairment and associated factors in Thai patients with schizophrenia. MATERIAL AND METHOD: A descriptive cross-sectional study of patients with schizophrenia that were selected consecutively from a psychiatric outpatient clinic at Srinagarind Hospital, Khon Kaen University between June 2008 andDecember 2009 was conducted. The Montreal Cognitive Assessment-Thai version (MoCA-T) test was used to evaluate cognitive functions. Associated factors such as age of onset, type of antipsychotics were assessed by collecting data from medical records. Data analysis used descriptive statistics, and univariate analysis used Chi-square. RESULTS: Seventy-five patients with schizophrenia were recruited The majority of cases was single, male, had low education, and manifested paranoia. The prevalence of cognitive impairment was 81.3%. Significant factors associated with cognitive impairment were the year of education lower than 12 (OR = 9.25, 95% CI 1.90-45.03, p = 0.002) and those who had taken typical and combined antipsychotic drugs (OR = 5.97, 95% CI 1.66-21.55, p = 0.005). CONCLUSION: Thai patients with schizophrenia showed a high prevalence of cognitive impairment. Therefore, clinicians should assess cognitive function and cognitive remedy
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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 | 0.002 |
| 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".