The Dépistage Cognitif de Québec: A New Clinician’s Tool for Early Recognition of Atypical Dementia
Bibliographic record
Abstract
INTRODUCTION: Early recognition of atypical dementia remains challenging partly because of lack of cognitive screening instruments precisely tailored for this purpose. METHODS: We assessed the validity and reliability of the Dépistage Cognitif de Québec (DCQ; www.dcqtest.org), a newly developed cognitive screening test, to detect atypical dementia using a multicenter cohort of 628 participants. Sensitivity and specificity were compared to the Montreal Cognitive Assessment (MoCA). A predictive diagnostic algorithm for atypical dementia was determined using classification tree analysis. RESULTS: The DCQ showed excellent psychometric properties. It was significantly more accurate than the MoCA to detect atypical dementia. All correlations between DCQ indexes and standard neuropsychological measures were significant. A statistical model distinguished typical from atypical dementia with a predictive power of 79%. DISCUSSION: The DCQ is a better tool to detect atypical dementia than standard cognitive screening tests. Expanding the clinician's tool kit with the DCQ could reduce missed/delayed identification of atypical dementia and accelerate therapeutic intervention.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".