Canadian Consensus Conference on Diagnosis and Treatment of Dementia (CCCDTD)5: Guidelines for management of vascular cognitive impairment
Bibliographic record
Abstract
INTRODUCTION: Vascular disease is a common cause of dementia, and often coexists with other brain pathologies such as Alzheimer's disease to cause mixed dementia. Many of the risk factors for vascular disease are treatable. Our objective was to review evidence for diagnosis and treatment of vascular cognitive impairment (VCI) to issue recommendations to clinicians. METHODS: A subcommittee of the Canadian Consensus Conference on Diagnosis and Treatment of Dementia (CCCDTD) reviewed areas of emerging evidence. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used to assign the quality of the evidence and strength of the recommendations. RESULTS: Using standardized diagnostic criteria, managing hypertension to conventional blood pressure targets, and reducing risk for stroke are strongly recommended. Intensive blood pressure lowering in middle-aged adults with vascular risk factors, using acetylsalicylic acid in persons with VCI and covert brain infarctions but not if only white matter lesions are present, and using cholinesterase inhibitors are weakly recommended. CONCLUSIONS: The CCCDTD has provided evidence-based recommendations for diagnosis and management of VCI for use nationally in Canada, that may also be of use worldwide.
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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 | 0.048 | 0.109 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.014 | 0.006 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".