P2‐197: Dépistage cognitif de québec (DCQ): A novel cognitive screening test for atypical dementias
Bibliographic record
Abstract
Recognition of the clinicoanatomical heterogeneity of dementing disorders has led to a major revision of their nosology in the last decade. With the potential emergence of disease-specific therapies, important efforts have been directed at correctly subtyping the patients among these newly described syndromes, mainly with neuroimaging tools. Conversely, cognitive screening tests have not kept the pace with this evolving nosology, being mainly targeted on diagnosing typical Alzheimer's disease (AD). As a result, while MMSE and MoCA are excellent tests for the screening and follow-up of typical AD, no validated screening tool truly helps clinicians in screening and subtyping atypical syndromes such as primary progressive aphasia or frontotemporal dementias. We present the Dépistage Cognitif de Québec (DCQ), a novel cognitive test aimed at improving the diagnosis of atypical/complex dementias in tertiary care memory clinics. Based on a systematic review of the literature on cognitive screening tools and focused groups with Canadian experts in dementia, we elaborated a tool based on five domains: 1) Memory, 2) Visuospatial, 3) Executive, 4) Language, and 5) Behaviour. We administered the DCQ and the MoCA to 180 normal aged individuals (age 50-90, mean 67 y-o) to generate normative data and assess its psychometric properties. The DCQ is a 30-minutes questionnaire that can easily be administered by a nurse. The questionnaire has adequate convergence validity with established measures of memory, visuospatial, executive and behavioural skills (Pearson coefficients between r=0.67 and r=0.95 p<0.01). Cronbach's alpha is high at 0.82 suggesting good internal consistency. Test-retest reliability at 30 days in 30 participants is very high (0.89) and inter-rater reliability using three different clinicians is excellent (0.96). The DCQ currrently only exists in French.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".