Effect of 3D-MOT training on the execution of manual dexterity skills in a population of older adults with mild cognitive impairment and mild dementia
Bibliographic record
Abstract
Computerized cognitive training tools are an alternative to preventive treatments related to cognitive impairment and aging. In this study, the transfer of 3D multiple object tracking (3D-MOT) training on manual dexterity concerning fine and gross motor skills in 38 elderly participants, half of them with mild cognitive impairment (MCI) and the other half with mild dementia (MD) was explored. A total of 36 sessions of the 3D-MOT training program were administered to the subjects. The Montreal Cognitive Assessment (MoCA) test was used to assess the baseline cognitive status of the participants. Two batteries of manual motor skills (GPT and MMDT) were applied before and after the 3D-MOT training program. The results showed an interaction effect of training and improvement in manual dexterity tests, from the first training session until the fifteenth session, and after this range of sessions, the interaction effect was lost. However, the training effect continued to the end of the thirty-six-session program. The experimental results show the effect of cognitive training on the improvement of motor skills in older adults. This type of intervention could have a broad impact on the aging population in terms of their attention, executive functions, and therefore, their quality of life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".