Validation of the Dépistage Cognitif de Québec: A New Cognitive Screening Tool for Atypical Dementias
Bibliographic record
Abstract
OBJECTIVE: This study aimed to validate and provide normative data for the Dépistage Cognitif de Québec (DCQ; www.dcqtest.org), a new cognitive screening tool for atypical dementias. METHOD: The DCQ was developed by expert behavioral neurologists and clinical neuropsychologists based on updated criteria for Alzheimer's disease, primary progressive aphasia, and behavioral variant frontotemporal dementia. It targets five relevant domains: Memory, Visuospatial, Executive, Language, and Behavior. Validation was performed in a population-based sample of 410 healthy French-speaking Canadians aged between 50 and 89 years old. Normative data were derived from a subsample of 285 participants. RESULTS: Mean DCQ total score (out of 100) was 89.17 (SD = 7.36). Pearson's correlation coefficient showed a strong and significant correlation (r = .71, p < .001) with the Montreal Cognitive Assessment. Internal consistency for the cognitive domains assessed by Cronbach's alpha was satisfactory (.74). Test-retest reliability was adequate (Pearson's coefficient = . 70, p < .001) and interrater reliability, excellent (intraclass correlation = .99, p < .001). Normative data shown in percentiles were stratified by age and education. CONCLUSIONS: This study suggests that the DCQ is a valid and reliable cognitive screening test. Application of the DCQ on populations with atypical dementias is underway to derive sensitivity and specificity values for various dementias.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".