Effects of online computerized cognitive training program Beynex on the cognitive tests of individuals with subjective cognitive impairment (SCI) and Alzheimer disease on rivastigmine therapy
Bibliographic record
Abstract
Background/aim: Clinical trials conducted on the efficacy of computerized cognitive training (CCT) programs have not led to any important breakthroughs. CCT is a safe and inexpensive approach, but its efficacy in patients on rivastigmine therapy has not been evaluated. This study aims to compare effects of CCT and examines rivastigmine to determine whether CCT has any further contributions to make. Materials and methods: Sixty individuals with subjective memory complaint (SCI) and 60 individuals with early stage Alzheimer’s dementia (AD) were subjected to the Montreal Cognitive Assessment (MoCA), Cambridge Cognition (CANTAB tests: MOT, PRM, DMS, SWM, PAL, RTI), and Bayer-ADL. After screening patients who were diagnosed with AD, we started rivastigmine patch treatment (10 cm2 = 9.5 mg). The SCI and AD groups were randomly divided, and one each of the SCI and AD groups were accessed using BEYNEX, a web-based program. After a minimum of at least 1200 min of use, the diagnostic tests were repeated. Results: The AD groups’ MoCA scores of the BEYNEX-practicing group demonstrated meaningfully increase, whereas they decreased in the control group, and the Bayer-ADL scores indicated improvement in ADL. The CANTAB tests both in SCI and AD and in groups using BEYNEX showed positive improvement in MOT, DMS, and PAL data. Conclusion: This study is a rare example that focuses on both groups with SCI and AD. The efficacy of CCT varies across cognitive domains and shows significant efficacy for AD but small improvements in cognitively healthy older adults. In future studies, integration with a smart learning algorithm may lead to interesting observations on which parameters are more sensitive to change under long-term use of CCT in a large number of subjects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".