The winding path towards rationale anti-thrombotic therapy to prevent stroke in patients with atrial fibrillation
Bibliographic record
Abstract
This editorial refers to ‘Characteristics of patients with atrial fibrillation prescribed antiplatelet monotherapy compared with those on anticoagulants: insights from the GARFIELD-AF registry’†, by F.W.A. Verheugt et al., on page 464. Stroke is a common and devastating complication of atrial fibrillation (AF). However, the randomized trials of warfarin represented a major turning point, demonstrating that two-thirds of strokes could be prevented.1 However, the uptake of this highly effective, evidence-based therapy was modest, due to the difficulties in prescribing and maintaining warfarin therapy, and the risks of major bleeding, including intracranial bleeding. To get around these limitations, a variety of anti-platelet regimes were studied as alternatives, including aspirin monotherapy, aspirin plus fixed, low-dose warfarin, and dual anti-platelet therapy with aspirin and clopidogrel.1 , 2 However, the reduction in stroke was only modest with aspirin compared with placebo, and aspirin in combination with either low-dose warfarin or clopidogrel was inferior to warfarin.1 Although the aspirin/clopidogrel combination was more effective at preventing thrombo-embolism than aspirin alone, it was associated with a significant increased risk of major bleeding.2 However; given its ease of use and perceived safety, aspirin use continued as stroke preventive therapy for many patients with AF in the 1990s and 2000s.3
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 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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.022 | 0.043 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".