Expanding the Applicability of Cognitive Charts to the Entire Age Span
Bibliographic record
Abstract
Background/Objectives: We previously developed Cognitive Charts (CCs) for early detection and/or longitudinal evaluation of age-associated cognitive decline on widely used cognitive screening measures such as the Mini-Mental State Examination (CC-MMSE) and the Montreal Cognitive Assessment (CC-MoCA). Similar to growth curves used in Pediatrics, clinicians can quickly interpret an individual’s performance on the MMSE or MoCA, track the patient’s longitudinal cognitive trajectory, and subsequently intervene earlier based on the findings (see quoco.org). This has proven very helpful to frontline clinicians, particularly in light of the newly approved monoclonal antibodies for treatment of Alzheimer’s disease. To this date, however, the CC-MMSE and CC-MoCA only applied to limited age ranges. We validated herein our CCs across the entire age span. Methods: Two datasets were obtained from the National Alzheimer’s Coordinating Center, for a total of 32,560 individuals. We examined average MMSE and MoCA scores for younger individuals compared to the current age thresholds and ensured consistency of age-related Cognitive Quotient scores. Results: In this study, both MMSE and MoCA scores show very little variation below the age threshold. If the age is fixed at the threshold in the QuoCo calculation, the resulting score remains constant within this range. Furthermore, CCs performed similar or better in younger individuals. Conclusions: Our findings again emphasize the clinical significance of CCs as a tool for monitoring cognitive changes across the entire age span, hence maximizing early detection and appropriate treatment monitoring.
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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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".