Management of Anticoagulant Treatment and Anticoagulation-Related Complications in Nonagenarians
Bibliographic record
Abstract
Given the aging population, the burden of age-dependent diseases is growing. Despite this, elderly patients are often underrepresented in clinical trials and little data are available on current anticoagulant management and outcomes in this unique population, especially those aged 90 years or older. There is uncertainty, and a fear of "doing harm," that often leads to de-prescription of antithrombotic agents in nonagenarian patients. Decision-making concerning the use of anticoagulant treatment needs to balance the risk of thrombotic events against the risk of major bleeding, especially intracranial hemorrhage. In this perspective, the development of direct oral anticoagulants (DOACs), acting as direct and selective inhibitors of a specific step or enzyme of the coagulation cascade, has dramatically changed oral anticoagulant treatment. In fact, given the lower incidence of intracranial hemorrhage, the favorable overall efficacy and safety, and the lack of routine monitoring, DOACs are the currently recommended anticoagulant agents for the treatment of both atrial fibrillation and venous thromboembolism even in very elderly patients. However, given the limited data available on the management of anticoagulation in nonagenarians, a few unanswered questions remain. In this review, we focused on recent evidence for anticoagulant treatment in atrial fibrillation and venous thromboembolism along with management of anticoagulation-related bleeding in nonagenarians.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".