Bibliographic record
Abstract
Purpose : This study aimed to present normative data for older Korean adults completing the Yonsei dual task cognitive screening test (Y-DuCog) and identify changes in cognitive function on the Montreal Cognitive Assessment - Korean (MoCA-K) with age. Methods : From May 2019 to August 2019, 195 healthy adults aged ≥60 years participated in this study. All participants completed the Y-DuCog to assess their dual-task performance and the MoCA-K to assess their cognitive function. Participants were divided into three groups based on their age: 60~69 years, 70~79 years, and ≥80 years. Results : The results of the Y-DuCog showed that dual-task performance time, effect, and correct response rate decreased significantly with age (p<.001). Scores from the three groups showed differences on all items (p<.001). Cognitive function on the MoCA-K also decreased significantly with age (mean score ± standard deviation [SD]; 27.33 ± 2.61 in subjects aged 60~69 years; 24.82 ± 3.20 in subjects aged 70~79 years; and 22.10 ± 4.91 in subjects aged ≥80 years; p<.001). Conclusions : Occupational therapists should be aware of the decline in cognitive function and dual-task performance time, effect, and correct response rate in older adults and consider interventions to treat this decline. Further studies are needed with larger groups of participants to examine factors, such as sex and education, that may impact dual-task performance and cognitive function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".