C-TOC (Cognitive Testing on Computer)
Bibliographic record
Abstract
INTRODUCTION: Cognitive Testing on Computer (C-TOC) is a novel computer-based test battery developed to improve both usability and validity in the computerized assessment of cognitive function in older adults. METHODS: C-TOC's usability was evaluated concurrently with its iterative development to version 4 in subjects with and without cognitive impairment, and health professional advisors representing different ethnocultural groups. C-TOC version 4 was then validated against neuropsychological tests (NPTs), and by comparing performance scores of subjects with normal cognition, Cognitive Impairment Not Dementia (CIND) and Alzheimer disease. C-TOC's language tests were validated in subjects with aphasic disorders. RESULTS: The most important usability issue that emerged from consultations with 27 older adults and with 8 cultural advisors was the test-takers' understanding of the task, particularly executive function tasks. User interface features did not pose significant problems. C-TOC version 4 tests correlated with comparator NPT (r=0.4 to 0.7). C-TOC test scores were normal (n=16)>CIND (n=16)>Alzheimer disease (n=6). All normal/CIND NPT performance differences were detected on C-TOC. Low computer knowledge adversely affected test performance, particularly in CIND. C-TOC detected impairments in aphasic disorders (n=11). DISCUSSION: In general, C-TOC had good validity in detecting cognitive impairment. Ensuring test-takers' understanding of the tasks, and considering their computer knowledge appear important steps towards C-TOC's implementation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".