Montreal Cognitive Assessment as a screening instrument for cognitive impairments in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is one of the core features of schizophrenia. For its evaluation, current clinical practice relies on detailed neuropsychological batteries which require trained testers and considerable amount of time to administer. Therefore, a brief and reliable screening tool for identification of overall cognitive impairment prior to a detailed comprehensive neurocognitive assessment is needed in a busy clinical setting. This study evaluates the clinical utility of the Montreal Cognitive Assessment (MoCA) in detecting cognitive impairments in schizophrenia and its relationship with functional outcome and demographic characters. METHODS: The MoCA, the Brief Assessment of Cognition in Schizophrenia (BACS), and the Brief UCSD Performance-based Skills Assessment (UPSA-B) were administered to 64 patients with schizophrenia. Mild and severe cognitive impairments were defined as BACS Z-score (calculated with the age and gender adjustments using previously published local norm data) of one or two standard deviations below the mean, respectively. RESULTS: The results showed that the MoCA was significantly correlated with BACS (r=.61, p<.001) and sensitive to detect both mild (AUC=0.82, p<.001) and severe (AUC=0.81, p<.001) cognitive impairments in schizophrenia. The MoCA was significantly correlated with UPSA-B score (r=.51, p<.001), and accounted for significant additional variance in UPSA-B score beyond the BACS. CONCLUSION: These findings indicate that MoCA is a useful bedside cognitive screening instrument for 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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".