Everyday functioning as a predictor of cognitive status in a group of community‐dwelling, predominantly Black adults
Bibliographic record
Abstract
INTRODUCTION: We examined whether the Performance Assessment of Self-Care Skills (PASS) and Everyday Cognition Scale-12 (ECog-12) dichotomized cognitive groups in a sample of predominantly Black adults. METHODS: Two hundred forty-six community-dwelling adults (95% Black, age 50+) completed cognitive testing, the PASS, and the ECog. Cognitive groups (probable vs unlikely cognitive impairment) were determined by performance on the Modified Mini-Mental State Examination. We examined the predictive validity of the PASS shopping, medication management, and information retrieval subtests and the ECog-12 to dichotomize cognitive groups. RESULTS: = 0.17). Only the PASS shopping and medication management had good reliability for determining cognitive group (areas under the curve (AUCs) of .74 each). DISCUSSION: PASS shopping and medication management exhibited adequate predictive validity when distinguished between cognitive status groups, whereas the PASS information retrieval and ECog-12 did not. Highlights: Mild functional decline is a core diagnostic criterion for cognitive impairment.Performance-based assessments are a valuable tool for assessing functional decline.Most performance-based measures were developed using homogenous samples.Few studies have validated these measures in other racial and ethnic populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".