The increasing impact of cerebral amyloid angiopathy: essential new insights for clinical practice
Bibliographic record
Abstract
Cerebral amyloid angiopathy (CAA) has never been more relevant. The last 5 years have seen a rapid increase in publications and research in the field, with the development of new biomarkers for the disease, thanks to advances in MRI, amyloid positron emission tomography and cerebrospinal fluid biomarker analysis. The inadvertent development of CAA-like pathology in patients treated with amyloid-beta immunotherapy for Alzheimer's disease has highlighted the importance of establishing how and why CAA develops; without this information, the use of these treatments may be unnecessarily restricted. Our understanding of the clinical and radiological spectrum of CAA has continued to evolve, and there are new insights into the independent impact that CAA has on cognition in the context of ageing and intracerebral haemorrhage, as well as in Alzheimer's and other dementias. While the association between CAA and lobar intracerebral haemorrhage (with its high recurrence risk) is now well recognised, a number of management dilemmas remain, particularly when considering the use of antithrombotics, anticoagulants and statins. The Boston criteria for CAA, in use in one form or another for the last 20 years, are now being reviewed to reflect these new wide-ranging clinical and radiological findings. This review aims to provide a 5-year update on these recent advances, as well as a look towards future directions for CAA research and 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".