Functional cognitive performance augments cognitive screening data in older adults
Bibliographic record
Abstract
Background Understanding the association of older adults’ cognitive ability with performance of instrumental activities of daily living (IADL) is critical to identifying their community health care support needs. We compared differences in performance-based IADL assessment scores among older adults according to their cognitive ability as measured by the Montreal Cognitive Assessment (MoCA). Methods Using data from a larger study we performed a cross-sectional analysis of 259 community-dwelling adults aged 55–93 years. Participants were categorized into one of three groups based on their MoCA score: mildly impaired (19–22), borderline (23–25), or unimpaired (26–30). The Performance Assessment of Self-care Skills Checkbook Balancing and Shopping Task (PCST) and the Weekly Calendar Planning Activity 17-item version (WCPA-17) were used to assess IADL. A MANCOVA analyzed the effect of MoCA group on the performance-based IADL assessments while controlling for education. Results The MANCOVA was statistically significant, F(4, 508) = 16.445, p < 0.001; Wilks’ λ = 0.784; ηp2 = 0.115. Follow-up univariate ANCOVAs showed that PCST Total Cues adjusted mean score [F(2, 255) = 20.006, p < 0.001; ηp2 = 0.136] and WCPA-17 Accuracy adjusted mean scores [F(2, 255) = 23.216, p < 0.001; ηp2 = 0.154] were significantly different among MoCA groups, with medium-large effect sizes. Conclusion The tripartite group categorization of the MoCA largely parallels ability on two independent performance-based IADL assessments, a subset of individuals borderline or unimpaired on the MoCA had difficulties with complex IADL identified by performance-based IADL assessments indicating comprehensive evaluations of older adults would benefit from including both types of assessments.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".