Stroke prevention in elderly patients with atrial fibrillation: challenges for anticoagulation
Bibliographic record
Abstract
Sinnaeve PR, Brueckmann M, Clemens A, Oldgren J, Eikelboom J, Healey JS (University Hospitals Leuven, Leuven, Belgium; Global Clinical Development and Medical Affairs, Ingelheim am Rhein, Germany; Uppsala University, Uppsala, Sweden; and Population Health Research Institute, Hamilton, Canada). Stroke prevention in elderly patients with atrial fibrillation: challenges for anticoagulation (Review). J Intern Med 2012; 271: 15–24. Abstract. Elderly patients with atrial fibrillation (AF), who constitute almost half of all AF patients, are at increased risk of stroke. Anticoagulant therapies, especially vitamin K antagonists (VKA), reduce the risk of stroke in all patients including the elderly but are frequently under‐used in older patients. Failure to initiate VKA in elderly AF patients is related to a number of factors, including the limitations of current therapies and the increased risk for major haemorrhage associated with advanced age and anticoagulation therapy. Of particular concern is the risk of intracranial haemorrhages (ICH), which is associated with high rates of mortality and morbidity. Novel oral anticoagulant agents that are easier to use and might offer similar or better levels of stroke prevention with a similar or reduced risk of bleeding should increase the use of antithrombotic therapy in the management of elderly AF patients. Amongst these new agents, the recently approved direct thrombin inhibitor dabigatran provides effective stroke prevention with a significant reduction of ICH, and enables clinicians to tailor the dose according to age and haemorrhagic risk.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".