Superiority of the Dépistage Cognitif de Québec (DCQ) over the Montreal Cognitive Assessment in differentiating dementia with Lewy bodies from Alzheimer's disease
Bibliographic record
Abstract
Abstract Background Recent criteria for dementia with Lewy bodies (DLB) are available but early diagnosis remains a challenge since few cases display all core clinical features. Discriminating Alzheimer’s disease (AD) from DLB is also complex given the overlap in cognitive dysfunctions between the two conditions. We aimed to study their differential neurocognitive profile using the Dépistage Cognitif de Québec (DCQ), a newly developed screening tool for atypical dementias. Moreover, we explored whether the DCQ was superior to the Montreal Cognitive Assessment (MoCA) in identifying DLB. Methods We compared the performance of 15 patients with DLB to 72 patients with the amnestic variant of AD on the five indexes of the DCQ (Memory, Visuospatial, Executive, Language and Behavioral) as well as the MoCA test, administered in random order. Participants were recruited prospectively in a tertiary memory clinic and diagnosed according to current clinical criteria for AD and DLB. Results There were no significant differences between the groups in age (AD: 72.97; DCL: 72.13) or level of education (AD: 12.88; DCL: 12.40). Mean Total MoCA score was similar across conditions (19.01 vs 20.00, p = .54) whereas the Total DCQ score revealed a significant difference between AD and DLB (66.39 vs 72.60, p < .05) (Fig. 2). Further analyses showed superior Area Under the Curve for the DCQ (69.23%; moderately strong) over the MoCA (55.26%; weak) in discriminating DCL from AD (Fig. 1). Bivariate analyses on the DCQ and MOCA indexes were performed using Wilcoxon‐Mann‐Whitney tests. When compared to AD, DLB participants performed worse on the Visuospatial Index (5.04 vs 4.10, p < .05) (Fig. 4), particularly the visuospatial construction task (1.33 vs 0.67 p < .05) while AD participants were significantly more affected on the Memory Index (recall and recognition) (16.13 vs 24.73, p < .0001) (Fig. 3). The remaining DCQ indexes (Executive, Language and Behavioral) were similar between groups. Conclusions Our data suggests that the DCQ may be a superior tool than the MoCA in distinguishing AD from DLB. The DCQ allowed better characterization of the neurocognitive profile between AD and DLB, where DLB preferentially affects visuospatial skills while AD targets memory abilities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".