Home‐based evaluation of executive function (Home‐MET) for older adults with mild cognitive impairment
Bibliographic record
Abstract
Abstract Background Executive function helps older adults maintain their activities of daily living by making plans, setting goals, and carrying them out successfully. It is important for their independence in community living. Clinically, the underlying deficits in executive function (EF) also significantly contributed to their functional disabilities. EF has shown to be important, especially in the fast and complicated contemporary world, because it has been considered as central to independent daily living, solve problems, and resolve conflicts. EF was considered as a vital part of the independent living skills of older adults with dementia in community living. Some researchers commented that executive function is the core psychological deficit underlying dysfunction of mild cognitive impairment. The resulting functional disabilities were the major cause of extended hospital stays and protracted residential care. Method With a carefully match‐group of 80 mild cognitive impaired with 80 health control subjects. The home‐based evaluation of executive function (Home‐MET) was validated in subjects’ own living environment. Result This Home‐MET showed significant correlation in the assessment of attention control that was assessing by Test of Everyday Attention (TEA) (r = .86, p < .01), with working memory that was assessed with Trail Making Test (TMT) (r = .72, p < .01), with inhibitory control that was assessing with Stroop Test (r = .86, p < .01), with individuals’ functional disability was assessed by Chinese Disability Assessment of Dementia (CDAD) (r = .77, p < .01) and cognitive assessment was assessed by Hong Kong Montreal Cognitive Assessment (HK‐MoCA) (r = .88, p < .01). By benchmarking with the validated performance‐based executive function assessment, the Home‐MET shows significant correlation (r = .92, p < .05) with the executive function test in a standard environment in hospital, i.e. the Chinese Multiple Errands Test (the Chinese‐MET). The two‐stage hierarchical linear regression model with backward method showed functional disability was a marginally significant predictor (p < .059) for the Home‐MET with regression model showed with R2 = .93. Conclusion Results indicated the Home‐MET, can provide an objective measure of executive function for subjects with mild cognitive impairment in participants’ own home environment.
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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 | 0.000 | 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".