Bibliographic record
Abstract
Abstract Cerebral small vessel disease can cause either ischemic stroke or intracerebral hemorrhage. Accounting for up to 25% of all strokes, it is also the second biggest contributor to the risk of dementia, and is the most common incidentally discovered finding on brain imaging. There are two main causes of cerebral small vessel disease: arteriolosclerotic small vessel disease (with hypertension as the main modifiable risk factor) and cerebral amyloid angiopathy (predominantly caused by β-amyloid deposits limited to the cerebral small arteries, arterioles, and capillaries). Prevention should include the treatment of hypertension and diabetes, if present, and the modification of lifestyle factors such as obesity and poor nutrition. Patients with small subcortical ischemic strokes should be treated with antithrombotics; dual antiplatelet therapy may be more effective than aspirin for the first 3 weeks following acute stroke, but is not more effective than aspirin for long-term prevention. Unresolved questions include the effectiveness of nonaspirin prevention strategies to prevent early recurrence or stroke extension in small subcortical ischemic stroke, and whether symptomatic or silent small vessel disease should influence decisions regarding selection for carotid revascularization or anticoagulation for atrial fibrillation. There is an unmet need for disease-modifying preventive therapies for cerebral amyloid angiopathy, the second most-common cause of spontaneous intracerebral hemorrhage.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".