Cardiovascular Management in Asymptomatic (Silent) Cerebral Microbleeds and Suspected Cerebral Amyloid Angiopathy
Bibliographic record
Abstract
Cerebral microbleeds (CMBs) detected on blood-sensitive magnetic resonance imaging sequences are usually a sign of an underlying cerebral small vessel disease such as sporadic cerebral amyloid angiopathy or sporadic nonamyloid small vessel pathology (eg, arteriolosclerosis). Much of the enduring interest in CMBs relates to their high prevalence (partly due to the widespread use of magnetic resonance imaging) in the context of stroke, cognitive impairment and in healthy individuals, and the clinical uncertainties created about the safety of antithrombotic medications due to their association with both future hemorrhagic and ischemic stroke. Historically, the research literature overwhelmingly emphasized the future hemorrhagic risk associated with CMBs, potentially leading to unnecessary withholding of treatments proven effective at preventing thrombosis, such as anticoagulants in patients with atrial fibrillation who happened to have some microbleeds. The lack of strong guidelines in this area contributes to wide variation in clinical practice. In this article, we critically review and discuss the implications of silent CMBs and cortical superficial siderosis (ie, without symptomatic intracerebral hemorrhage) in different clinical settings: the general population, patients with ischemic stroke, and the memory clinic. Emerging evidence, albeit not from randomized controlled trials, suggests that in most patients, CMBs alone should not prevent the use of antithrombotics or anticoagulants for stroke prevention, when they are otherwise indicated. Where possible, we provide specific suggestions for clinical care grounded in both the limited available literature and our personal clinical practice.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".