Validity of Computer Based Administration of Cognitive Assessments compared to Traditional Paper-based Administration
Bibliographic record
Abstract
Traditional pen and paper based neuropsychological tests (NPT) for cognition assessment have several challenges limiting their use. They are time consuming, expensive, and require highly trained specialists to administer. This leads to testing being available to only a small portion of the population and often with wait times of several months. In clinical practice, we have found results tend not to be integrated effectively into assessment and plans of the ordering provider. Here we compared several tests using BrainCheck (BC), a computer-based NPT battery, to traditional paper-based NPT, by evaluating individual tests as well as comparing composite scores to scores on traditional screening tools. 26 volunteers took both paper-based tests and BC. We found scores of four assessments (Ravens Matrix, Digit Symbol Modulation, Stroop Color Word Test and Trails Making A&B Test) were highly correlated. The Balance Examination and Immediate/Delayed Hopkins Verbal Learning, however, were not correlated. The BC composite score was correlated to results of the Saint Louis University Mental Status (SLUMS) exam [1], the Mini-Mental State Examination (MMSE) [2], and the Montreal Cognitive Assessment (MoCA). Our results suggest BC may offer a computer-based avenue to address the gap between basic screening and formal neuropsychological testing.
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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 | 0.028 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".