Correlation Between MoCA and MMSE For the Assessment of Cognition in Schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Schizophrenia (Sch) is a complex neurodevelopmental disorder associated with impairment of cognitive function as a central feature, which is confirmed by a number of studies performed on patients suffering from Sch, where clinical symptoms and social functioning of patients are consequences of neurocognitive deficits. GOAL: The goal of this study was to assess the clinical usability of the Montreal Cognitive Assessment (MoCA) as a screening instrument for cognitive impairment in schizophrenic patients, alone and in correlation with the Mini-Mental State Examination (MMSE). MATERIAL AND METHODS: This clinical prospective study included 30 patients diagnosed with schizophrenia. Patients were selected from Psychiatric Clinic, Clinical Center University of Sarajevo (CCUS) during 2010. For assessment of cognitive impairment we used Montreal Cognitive Assessment Scale (MoCA) and Mini-Mental State Examination (MMSE). RESULTS: From the total number of respondents (n=30), 15/30 (50 %) were males and 15/30 (50 %) were females; age of onset were 23.5±6.69; duration of illness before hospitalization (mean±SD) 32.5±12.9. If we make a comparison of MoCA scale and MMSE under the limit values, then we get that there was 10 true positive, 4 true negative, 14 false positive and 2 false negative. This all leads to sensitivity of MoCA scale again in comparison with the MMSE of 41.7%, specificity 66.7%, positive predictive value of 83.3% and negative predictive value of 22.2%. CONCLUSIONS: Our findings provide preliminary evidence that MoCA scale performs well in detecting true positive but it is imprecise in the detection of true negative findings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".