An optimal combination of MCCB and CANTAB to assess functional capacity in older individuals with schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Cognitive deficits predict functional capacity in patients with schizophrenia including in late life. The MATRICS Consensus Cognitive Battery (MCCB) and the Cambridge Neuropsychological Test Automated Battery (CANTAB) are widely used to assess cognition in this population. The aim of this study was to determine a minimal set of subtests across the two batteries that would be strongly associated with functional capacity in older patients with schizophrenia. METHODS: Sixty participants age 50 years or older with a diagnosis of schizophrenia or schizoaffective disorder and 30 control participants were enrolled. Cognition was assessed using the MCCB and the CANTAB. Functional capacity was assessed using the USCD Performance-based Skills Assessment (UPSA). Stepwise linear regressions were performed to determine the best set of cognitive tests associated with functional capacity. RESULTS: UPSA total score was negatively correlated with age and positively correlated with education and the MCCB global score. Most of the MCCB domains and subtests, and several of the CANTAB subtests correlated with UPSA total score. In the regression model, MCCB global score accounted for 42.5% of UPSA variance. In contrast, a combination of only four subtests (processing speed and verbal learning from the MCCB, and affective information processing and working memory from the CANTAB) accounted for 60% of UPSA variance. CONCLUSIONS: Performance on MCCB and CANTAB is strongly associated with functional capacity in older patients with schizophrenia. A selective combination of MCCB and CANTAB subtests may be as effective in assessing functional capacity in late life schizophrenia. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".