Neuropsychological Assessment: Digital vs. Paper Comparability in Older Adults
Bibliographic record
Abstract
Abstract The use of digital neuropsychological assessment has increased since the COVID-19 pandemic and continues to be essential (Crivelli, 2024). While digital administration may be more efficient, the accuracy of the results may be questionable (due to potential differences in performance, etc.). This study aimed to evaluate the comparability of brief neuropsychological tests in similar digital and paper forms. Participants completed two cognitive screening tests and two brief tests of executive function: the paper MoCA, and the digital Boston Cognitive Assessment (BoCA), as well as the paper Stroop task (“pStroop”), and an electronic version “eStroop,” respectively. A study conducted by Castelli et al. (2021) found that the paper cognitive screener: the Montreal Cognitive Assessment (MoCA), is a sensitive and specific test used to detect cognitive impairment. In this study, it was hypothesized that the performance of digital and paper-and-pencil assessments would be similar, evidenced by positive correlations. Twelve older adults with a mean age of 68.5 years (SD = 8.07) completed a combined lab protocol. Among older adults, the MoCA and the BoCA were significantly moderately correlated, r(10) = .62, p = .03, while the pStroop and eStroop were significantly strongly correlated, r(10) = .90, p = .002. This study aimed to determine if digital and paper-and-pencil neuropsychological tests performance would be comparable among brief screening tests. The results showed that for older adults, there was a moderate to strong correlation between paper-and-pencil and digital neuropsychological testing, suggesting that the use of electronic tests may, in some situations, be interchangeable.
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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 | 0.005 | 0.028 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".