Relationship between reaction time variability on go/no go tasks and neuropsychological functioning in younger and older adults
Bibliographic record
Abstract
Introduction Early detection of cognitive impairment in older adults is important for the prevention of dementia. Intra-individual variability in reaction time (IIV-RT) during go/no-go tasks can be used for the early detection of cognitive impairment in older adults living in the community. This study aimed to determine the relationship between IIV-RT and cognitive function during go/no-go tasks and the cutoff values for determining the risk of cognitive impairment in community-dwelling older adults.Methods This study included 31 older adults without cognitive impairment, 15 community-dwelling older adults with cognitive impairment, and 34 healthy young adults. All participants performed a go/no-go task to assess the IIV-RT. Additionally, older adults underwent neuropsychological testing. Based on the results of the Japanese version of the Montreal Test of Cognitive Abilities (MoCA-J), older adults were divided into those with normal cognition and those with cognitive impairment.Results There were significant differences in the IIV-RT among groups, including a higher IIV in the cognitively impaired group than in young adults and cognitively normal older adults. Moreover, the IIV-RT was correlated with the MoCA-J (r = −0.531, p < 0.001), Trail Making Test Part A (r = 0.571, p < 0.001), and Verbal Fluency Test scores (r = −0.442, p = 0.002). Receiver operating curve analysis showed that the area under the curve for IIV-RT was 0.935, and the cutoff value at which the IIV-RT identified cognitive impairment was 25.37%.Conclusions These findings indicate that the IIV-RT during go/no-go tasks is a useful early indicator of cognitive impairment in community-dwelling older adults.
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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 | 0.002 |
| 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 | 0.000 | 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".