Advances in Cerebral Small Vessel Disease: Sandra E. Black Lecture to the Canadian Neurological Sciences Federation
Bibliographic record
Abstract
Cerebral small vessel diseases (CSVDs) are among the most common age-related pathologies of the brain. Arteriolosclerosis and cerebral amyloid angiopathy (CAA) are the most common CSVDs. In addition to causing stroke and dementia, CSVDs can have diverse covert radiological manifestations on computed tomography and magnetic resonance imaging including lacunes, T2-weighted white matter hyperintensities, increased density of visible perivascular spaces, microbleeds and cortical superficial siderosis. Because they cannot be visualized directly, research on the pathophysiology of CSVD has been difficult. However, advances in quantitative imaging methods, including physiological imaging such as measurement of cerebrovascular reactivity and increased vascular permeability, are beginning to allow investigation of the early effects of CSVD in living people. Furthermore, genomics, metabolomics and proteomics have the potential to illuminate previously unrecognized pathways to CSVD that could be important targets for new clinical trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".