Screening for cognitive impairment in schizophrenia: A comparison between the Mini-Mental State Examination and the Montreal Cognitive Assessment Test
Bibliographic record
Abstract
Objective: Cognitive impairment is a core feature affecting social and occupational functionality in schizophrenia.The aim of this study is to compare the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in screening for cognitive impairment in individuals diagnosed with schizophrenia and to examine the relationship between neurocognitive functions and clinical symptoms.Method: The study included 135 individuals with schizophrenia followed in Ankara Dışkapı Community Mental Health Centre.Sociodemographic Data Form, Brief Psychiatric Rating Scale (BPRS), The Scale for The Assessment of Positive Symptoms (SAPS), Negative Symptoms Assessment Scale (SANS), MMSE and MoCA were administered.Results:The mean MMSE score was 25.64 ± 2.72, and the mean MoCA score was 17.91 ± 3.83.There was a high positive correlation between the MMSE and MoCA scores (r=0.667).The MMSE and MoCA tests showed a substantial difference in the assessment of cognitive functions; and MoCA was found more sensitive than the MMSE in determining cognitive impairment.Moreover, the MMSE and MoCA scores showed a negative correlation with the BPRS, SANS, and SAPS scores.Discussion: These findings indicate that MoCA may be used as a more useful screening test for cognitive impairment in people with 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".