Mapping Cognitive Trajectories and Detecting Early Dementia Using the Mini-Mental State Examination Cognitive Charts: Application to the French Three-City Cohort
Bibliographic record
Abstract
The Cognitive Quotient (QuoCo) classification algorithm monitoring decline on age- and education-adjusted Mini-Mental State Examination (MMSE)-derived cognitive charts has proved superior to the conventionally-used cut-off for identifying incident dementia; however, it remains to be tested in different settings. Data were drawn from the Three-City Cohort to 1) assess the screening accuracy of the QuoCo, and 2) compare its performance to that of serial MMSE tests applying different cut-offs. For the QuoCo, sensitivity was 74.2 (95% CI: 71.4-76.8) and specificity 84.1 (83.6-84.7) and for the MMSE < 24, 64.1 (61.1-67.0) and 94.8 (94.4-95.1), respectively; whereas overall accuracy and sensitivity was highest for MMSE cut-offs <25 and <26. User-friendly charts for mapping cognitive trajectories over visits with an alert for potentially 'abnormal' decline can be of practical use and encourage regular monitoring in primary care where the <24 cut-off is still widely used despite its poor sensitivity.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".