Development of a method for quantifying cognitive ability in the elderly through adaptive test administration
Bibliographic record
Abstract
BACKGROUND: The field of geriatric medicine has identified a need for an evaluative tool that can rapidly quantify global cognitive ability and accurately monitor change over time in patients with a wide range of impairments. We hypothesized that the development of an adaptive test approach to cognitive measurement would help to meet that need. This study aimed to provide evidence for the interpretability of scores obtained from a novel, adaptive approach to cognitive assessment, called the Geriatric Rapid Adaptive Cognitive Estimate (GRACE) method. METHODS: An adaptive method for cognitive assessment was developed using data from 185 patients referred for geriatric cognitive assessment, and pilot tested in an additional 137 patients. Correlations between test scores and between rank orders of patients were computed to examine the reliability and validity of cognitive ability scores obtained by (1) administering test questions out of their usual order, (2) administering only a subset of questions, and (3) administering questions adaptively using simplified selection rules based on the most difficult question passed. RESULTS: Cognitive ability scores obtained with the GRACE method correlated highly with the Montreal Cognitive Assessment (MoCA) scores (r = 0.93) and ranked patients similarly in order of ability (r > 0.87). A simplified adaptive testing algorithm for pencil-and-paper assessment demonstrated moderately high correlations with scores obtained from administering the full set of MMSE and MoCA items as well as the MoCA items alone. CONCLUSIONS: Scores from the GRACE method can be obtained easily in 5-10 minutes, reducing test burden. The resulting numeric score quantifies cognitive ability, allowing clinicians to compare patients and monitor change in global cognition over time. The adaptive nature of this method allows for evaluation of persons across a broader range of cognitive ability levels than currently available tests.
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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 | 0.015 | 0.044 |
| 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) | 0.002 | 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".