Bibliographic record
Abstract
Justin B Ng,1 Michele Turek,2,3 Antoine M Hakim4–7 1Departments of Neuroscience and Psychology, McGill University, Montreal, QC, Canada; 2Division of Cardiology, The Ottawa Hospital, Ottawa, 3Department of Medicine, University of Ottawa, Ottawa, 4Division of Neurology, The Ottawa Hospital, Ottawa, 5Brain and Mind Research Institute, University of Ottawa, Ottawa, 6Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, 7Canadian Stroke Network, Ottawa, ON, Canada Abstract: As life expectancy lengthens, dementia is becoming a significant human condition in terms of its prevalence and cost to society worldwide. It is important in that context to understand the preventable and treatable causes of dementia. This article exposes the link between dementia and heart disease in all its forms, including coronary artery disease, myocardial infarction, atrial fibrillation, valvular disease, and heart failure. This article also explores the cardiovascular risk factors and emphasizes that several of them are preventable and treatable. In addition to medical therapies, the lifestyle changes that may be useful in retarding the onset of dementia are also summarized. Keywords: Alzheimer’s disease, coronary artery disease, myocardial infarction, atrial fibrillation, valvular disease, heart failure, cardiovascular risk factors, prevention
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".