Feasibility of using the MoCA to detect subtle cognitive decline in oldest of old
Bibliographic record
Abstract
PROJECT SUMMARY: Pathophysiological processes that lead to Alzheimer’s disease (AD) likely begin many years before diagnosis, yet a clear biological and cognitive profile heralding these processes still remains elusive. Contributing to this unclear picture is the lack of a brief cognitive assessment tool that is sensitive, specific, and for which normative data exist for the most vulnerable age groups developing AD — the oldest old. Our work (presented in the Preliminary Results) and others’ suggest that early signs of dementia are being missed by the MMSE — the most widely used screen — resulting in delayed diagnosis and missed early treatment. Identifying eventual AD patients at the earliest stages of the disease is crucial in order to stave off the explosion of new cases expected with aging baby boomers. The Montreal Cognitive Assessment (MoCA) is a strong candidate for detecting early subtle signs of mild cognitive impairment (MCI). However, it is unknown how it performs in the oldest old individuals and against an accepted “gold standard” global cognitive assessment tool. Two aims will allow us to test the hypothesis that the MoCA is a sensitive instrument for detecting subtle cognitive decline in healthy, 80, 90, and 100 year olds. We will determine normative performance and cutoffs for these ages and determine how the MoCA performs against the DRS- 2, an established cognitive assessment tool used in research at ADCs. With these results, we propose to begin a new research program investigating whether combining biomarkers with sensitive behavioral measures can detect early cognitive decline in the preclinical stages of AD, a priority of the NIA. Our long- term goal is to better define the factors that best predict cognitive decline in biomarker-positive individuals in order to progress toward an accurate cognitive and biological profile of preclinical AD. Accomplishing the two aims will provide for the first time normative data on the MoCA for healthy community-dwelling oldest old individuals, an essential first step in detecting early signs of dementia by elderly patients’ primary care physicians. It will also move us toward a clearer picture of what healthy cognitive aging looks like. © Brian W. Leonard 2012
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".