Cognitive deficits characterization using the CogState Research Battery in first-episode psychosis patients
Bibliographic record
Abstract
The computer-based CogState Research Battery (CSRB) proposes a test structure which follows MATRICS recommended cognitive domains but lacks direct comparison to pen and paper batteries in first-episode psychosis (FEP). The aim of this study was to compare performances obtained with the CSRB and a pen and paper battery in a historical cohort of FEP patients. Among patients entering an early intervention program between 2003 and 2014, separate cohorts completed the traditional pen and paper cognitive battery (n = 182) and the CSRB (n = 97). Composite z-scores were derived using normative data of matched controls (n = 64 pen and paper, n = 69 CSRB) and were compared between the two batteries for the 7 cognitive domains. The cohort tested using the CSRB performed better on the domains of processing speed, attention, visual memory, and verbal memory than the cohort tested using the pen and paper battery (all p < 0.001). Performance did not differ between the two types of batteries for the working memory, executive functions, and social cognition domains. Cognitive profiles identified in the two patient cohorts were similar, with verbal memory being the most impaired domain. Better performances on the CSRB may be primarily due to the minimal demand of the computerized tests on graphomotor abilities and reading speed compared to the pen and paper tests. Our investigation offers a better understanding on how the results obtained with computerized batteries may compare to earlier work done with traditional tests.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".