Detecting cognitive decline in community‐dwelling older adults using simple cognitive and motor performance tests
Bibliographic record
Abstract
AIM: The objective of this study was to estimate the predictive accuracy of simple cognitive and motor performance tests to detect cognitive decline (CD) in community-dwelling older adults. METHODS: In total, 102 community-dwelling older adults participated in this study. Cognitive function, gait performance and coordinated finger movements were assessed using the Montreal Cognitive Assessment, the 10-m walking test and the finger-tapping test, respectively. We classified the participants into either a CD (n = 60) or a healthy control (n = 42) group. RESULTS: Significant group differences were found in the visuospatial/executive function subscale score on the Montreal Cognitive Assessment, stride length and total finger tap count. The results of multivariate logistic regression analysis showed that visuospatial/executive function subscale score, stride length and total tap count were the significant predictors for the presence of CD (sensitivity 83.3%, specificity 82.9%, predictive accuracy 83.2%). We also constructed a decision tree model with these three variables to increase the usefulness of our model as a screening tool by assigning a cut-off value for each assessment. The sensitivity and specificity of the model were 88.1% and 85.2%, respectively, with an overall predictive accuracy of 86.4%. CONCLUSIONS: The results of the present study suggest that simple cognitive and motor performance tests have moderate-to-high discriminant validity for the presence of CD in community-dwelling older adults. Addition of such tests might lead to the more accurate detection of early cognitive decline. Geriatr Gerontol Int 2020; ••: ••-••.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".