The Diagnosis of “Mixed” Dementia in the Consortium for the Investigation of Vascular Impairment of Cognition (CIVIC)
Bibliographic record
Abstract
If vascular risk factors are risk for Alzheimer's disease (AD), and if "pure" vascular dementia (VaD) is less common than has been thought, what do we make of the diagnosis of mixed dementia? We report characteristics of those with mixed dementia in a prospective, seven center, clinic-based Canadian study. Of 1,008 patients, 372 were diagnosed with AD, 149 with vascular cognitive impairment (VCI) including 76 with mixed AD/VaD, and 82 with other types of dementia. The mean age of patients with mixed AD/VaD was 78.0 +/- 7.6 years; 49% were female. These proportions differed significantly between dementia diagnosis subgroup (p < 0.001) showing a trend which is evident in all comparisons--AD/VaD patients fall in between AD and VaD. Vascular risk factors were present significantly more often in mixed AD/VaD than in AD (p < 0.001). More mixed AD/VaD (20%) than AD patients (4%) had focal signs, compared with 38% of those with vascular dementia and 12% with other types of dementia. Between the initial clinical diagnosis and the final diagnosis (which utilized neuroimaging and neuropsychological data) AD/VaD was the least stable diagnosis. Neuroimaging of ischemic lesions was the most common reason for reassignment from AD to the mixed AD/VaD diagnosis (17 cases). These data suggest that an operational definition of mixed AD/VaD can be proposed on presentation and clinical/radiographic findings, but indifferent to vascular risk factors. The concept of mixed dementia should be extended to include vascular dementia in combination with dementias, other than Alzheimer's disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".