Development of a method for quantifying cognitive ability in the elderly using adaptive testing
Bibliographic record
Abstract
With the impending aging of the Canadian population, the need for an assessment tool that can accurately measure cognitive ability in the elderly and monitor changes in cognition over time is rapidly gaining importance. The objective of the present study was to contribute evidence to the interpretability of scores from a novel cognitive assessment tool (GRACE) designed to be administered adaptively in the elderly population. Responses to items from two cognitive screening tests administered to patients attending a Geriatric Cognitive Disorders Clinic in Montreal were calibrated onto an interval scale using Rasch analysis. The hierarchy of items, organized by level of cognitive difficulty, was administered in a pilot adaptive format to a new cohort of patients, followed by administration of the remaining items to calculate total test scores. The reliability and validity of the GRACE method were demonstrated by comparing scores obtained from different orders of item administration (i.e. across cohorts) and by comparing scores with validated measures used in the clinic (i.e. across test methods), respectively. Additionally, demonstration of the validity of administering only a subset of items provided sound justification for developing and prospectively evaluating an optimal algorithm for adaptively administering test items. In addition to reducing test burden, the GRACE method provides a quantitative estimate of cognitive ability across the range of ability levels from normal to severe dementia and may be useful to clinicians as single tool to rapidly quantify and monitor cognitive ability.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".