A comparison between the Mini-Mental State Examination and the Montreal Cognitive Assessment Test in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is a core feature of schizophrenia that also has strong prognostic significance. In most clinical settings comprehensive neuropsychological testing to detect cognitive impairment in schizophrenia patients is not readily available, but because cognitive deficits in schizophrenia are clinically important it would be useful to detect or at least screen for them in a clinical setting. Unfortunately there are no validated, brief screening instruments for the detection of cognitive impairment in schizophrenia. Nevertheless, the Montreal Cognitive Assessment Test (MoCA) and the Mini-Mental State Examination (MMSE) show promise in this regard. The objective of this study was to compare the results of the MMSE and MoCA in a group of outpatient schizophrenia sufferers to contribute to research into the instruments' potential usefulness as screening tools for cognitive impairment in schizophrenia. METHOD: The Afrikaans versions of the MMSE and MoCA were administered to Afrikaans-speaking adult outpatients. Participants had at least seven years of formal education and had been in partial or full remission for at least 3 months. The MMSE and MoCA scores for each participant were matched and compared using the non-parametric Wilcoxon matched pairs test. RESULTS: = 0.000008). CONCLUSION: Compared to the MMSE, the MoCA may be a more useful instrument to detect cognitive impairment in patients with schizophrenia. Further studies are required.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".