Diagnosis and management of cerebral amyloid angiopathy: a scientific statement from the International CAA Association and the World Stroke Organization
Bibliographic record
Abstract
Cerebral amyloid angiopathy (CAA) is a well-recognized and challenging disease for neurologists and other clinicians caring for the rapidly aging worldwide population. CAA is a major cause of spontaneous lobar intracerebral hemorrhage (ICH), and can also cause transient focal neurological episodes, and convexity subarachnoid hemorrhage, CAA-associated ICH has a high mortality, morbidity, and recurrence rate. CAA can affect a wide range of clinical decisions including use of antithrombotic medications, safety for anti-β-amyloid peptide (Aβ) immunotherapy, and need for anti-inflammatory or immunosuppressive treatment. We present guidelines, intended to inform the approach to individuals with suspected CAA, written on behalf of the International CAA Association and the World Stroke Organization (WSO). We cover five areas selected for their relevance to practice: (1) diagnosis, testing, and prediction of intracerebral hemorrhage risk; (2) antithrombotic agents and vascular interventions; (3) vascular risk factors and concomitant medications; (4) treatment of CAA manifestations; and (5) diagnosis and treatment of CAA-related inflammation and vasculitis. The statement has been reviewed and approved by the Executive Committee of the WSO, and the International CAA Association.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".