It's all about cognitive trajectory: Accuracy of the cognitive charts– <scp>MoCA</scp> in normal aging, <scp>MCI</scp> , and dementia
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is an established cognitive screening tool in older adults. It remains unclear, however, how to interpret its scores over time and distinguish age-associated cognitive decline (AACD) from early neurodegeneration. We aimed to create cognitive charts using the MoCA for longitudinal evaluation of AACD in clinical practice. METHODS: We analyzed data from the National Alzheimer's Coordinating Center (9684 participants aged 60 years or older) who completed the MoCA at baseline. We developed a linear regression model for the MoCA score as a function of age and education. Based on this model, we generated the Cognitive Charts-MoCA designed to optimize accuracy for distinguishing participants with MCI and dementia from healthy controls. We validated our model using two separate data sets. RESULTS: For longitudinal evaluation of the Cognitive Charts-MoCA, sensitivity (SE) was 89%, 95% confidence interval (CI): [86%, 92%] and specificity (SP) 79%, 95% CI: [77%, 81%], hence showing better performance than fixed cutoffs of MoCA (SE 82%, 95% CI: [79%, 85%], SP 68%, 95% CI: [67%, 70%]). For current cognitive status or baseline measurement, the Cognitive Charts-MoCA had a SE of 81%, 95% CI: [79%, 82%], SP of 84%, 95% CI: [83%, 85%] in distinguishing healthy controls from mild cognitive impairment or dementia. Results in two additional validation samples were comparable. CONCLUSIONS: The Cognitive Charts-MoCA showed high validity and diagnostic accuracy for determining whether older individuals show abnormal performance on serial MoCAs. This innovative model allows longitudinal cognitive evaluation and enables prompt initiation of investigation and treatment when appropriate.
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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 | 0.019 | 0.073 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".