Atrieflimmer og hjerneslag
Bibliographic record
Abstract
BACKGROUND: More than 70,000 Norwegians have atrial fibrillation, which is a major risk factor for ischemic stroke. A large proportion of ischemic strokes caused by atrial fibrillation could be prevented if patients receive optimal prophylactic treatment. This article describes the risk for ischemic stroke in patients with atrial fibrillation, and discusses who should receive prophylactic treatment and which therapy provides the best prevention. METHOD: The article is based on recently published European, American and Canadian guidelines, a search in PubMed and the authors' own clinical experience. RESULTS: The new risk score CHA2DS2-VASc is better than the CHADS2 score for identifying patients with atrial fibrillation who have a truly low risk of ischemic stroke and are not in need of antithrombotic treatment. Oral anticoagulation therapy is recommended for patients with two or more risk factors for thromboembolism in addition to atrial fibrillation (CHA2DS2-VASc ≥ 2). Patients with atrial fibrillation and a single additional risk factor (CHA2DS2-VASc =1) an individual assessment should be made as to who should receive oral anticoagulants, and for patients with CHA2DS2-VASc = 0 antithrombotic treatment is not recommended. New oral anticoagulants are at least as effective as warfarin for preventing ischemic stroke in patients with nonvalvular atrial fibrillation, they carry a lower risk of cerebral haemorrhage, especially intracranial haemorrhage and are more practical in use. Platelet inhibitors have a minimal role in stroke prevention in patients with atrial fibrillation. INTERPRETATION: Risks stratifying patients using the CHA2DS2-VASc score is a better method for assessing which patients with atrial fibrillation who should receive oral anticoagulation. The introduction of new oral anticoagulants will simplify preventive treatment and hopefully lead to a more efficient anticoagulation treatment in a larger number of patients with atrial fibrillation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.010 |
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; both teacher heads agree on what is shown here.
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".