Computerized assessment of cognition in schizophrenia: Promises and pitfalls of CANTAB
Bibliographic record
Abstract
OBJECTIVE: Over the last decade, the Cambridge Neuropsychological Test Automated Battery (CANTAB), which comprises visuo-spatial tasks, has been utilized in cognitive studies of schizophrenia. A clear approach concerning the usage of CANTAB for the appraisal of neurocognitive dysfunction in schizophrenia is currently lacking. METHOD: In this paper, we have first reviewed the overall applications of CANTAB and then evaluated methodological strengths and weaknesses of CANTAB as a neurocognitive battery for schizophrenia. We carried out a systematic search and assessment of studies where CANTAB was utilized to measure cognitive function in schizophrenia. We have also attempted to quantify the available data and perform a meta-analysis, but this approach turned out to be still premature. RESULTS: CANTAB has enabled researchers to highlight significant deficits affecting broad cognitive domains in schizophrenia, such as working memory, decision-making, attention, executive functions and visual memory. So far, the most consistent deficit observed with CANTAB testing has been attentional set-shifting, suggestive of fronto-striatal dysfunctions. In addition, preliminary evidence points towards the potential use of CANTAB to identify cognitive predictors of psychosocial functioning, to describe the relationships between symptoms and cognition, and to measure the impact of pharmacological agents on cognitive functioning. CONCLUSION: CANTAB has been used successfully to highlight the range of visuo-spatial cognitive deficits in schizophrenia, producing similar results to those obtained with some traditional neuropsychological tests. Further studies validating the use of CANTAB following the standard set by Measurement And Treatment Research to Improve Cognition in Schizophrenia (MATRICS) are warranted.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".