VALIDATION OF A NEW FUNCTIONAL COGNITION SCREENING MEASURE
Bibliographic record
Abstract
Abstract Identification of cognitive deficits that contribute to functional dependence in acute and post-acute care settings is important. The Menu Task (MT) is a brief cognitive 12 item screening measure designed to identify older adults at risk for IADL impairment in community settings. The objective was to compare the psychometric properties of the Menu Task a series of neurocognitive and functional cognitive performance measures including the Brief Interview of Mental Status (BIMS), the Montreal Cognitive Assessment (MoCA) the Weekly Calendar Planning Activity (WCPA) and the Performance Assessment of Self-Care Skills (PASS) in a sample of 200 community dwelling older adults. Participants were administered the MT, BIMS, MoCA, WCPA, and the PASS checkbook and shopping tasks. ROC analysis identified an MT cut score, sensitivity and specificity. We computed Cronbach’s alpha, correlations among study measures and t-tests between groups impaired or unimpaired on the MT. Mean age of participants was 70.44(SD 8.3), the sample was predominately female ((76%), and white (81%). Mean MT score was 8.22 (SD 2.01) and the mean completion time was 185.5 sec (SD 108.11). The Menu Task has moderate internal consistency (α= 0.65). The AUC statistic was 0.83 with an optimal MT cut score of “7” and sensitivity of .90 and specificity of .70. Significant differences (p < 0.01) were observed between impaired and not impaired MT groups on BIMS, MoCA, WCPA, and PASS. The Menu Task has moderate to strong evidence supporting its psychometric properties and the value of screening for functional cognitive deficits.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".