Choosing a particular oral anticoagulant and dose for stroke prevention in individual patients with non-valvular atrial fibrillation: part 2
Bibliographic record
Abstract
The choice of oral anticoagulant (OAC) for patients with atrial fibrillation (AF) may be influenced by individual clinical features or by patterns of risk factors and comorbidities. We reviewed analyses of subgroups of patients from trials of vitamin K antagonists vs. non-vitamin K oral anticoagulants (NOACs) for stroke prevention in AF with the aim to identify patient groups who might benefit from a particular OAC more than from another. In addition, we discuss the timing of initiation of anticoagulation. In the second of a two-part review, we discuss the use of NOAC for stroke prevention in the following subgroups of patients with AF: (vii) secondary stroke prevention in patients after stroke or transient ischaemic attack (TIA), (viii) patients with acute stroke requiring thrombolysis or thrombectomy, (ix) those initiating or restarting OAC treatment after stroke or TIA, (x) those with renal impairment on dialysis, (xi) the elderly, (xii) those at high risk of gastrointestinal bleeding, and (xiii) those with hypertension. In addition, we discuss adherence and compliance. Finally, we present a summary of treatment suggestions. In specific subgroups of patients with AF, evidence supports the use of particular NOACs and/or particular doses of anticoagulant. The appropriate choice of treatment for these subgroups will help to promote optimal clinical outcomes.
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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 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".