Bibliographic record
Abstract
Atrial fibrillation (AF), the most common clinically relevant arrhythmia, affects 2.2 million individuals in the USA and 4.5 million in Europe, resulting in significant morbidity and mortality. Pharmacotherapy aimed at controlling both heart rate and rhythm is employed to relieve AF symptoms, though debate continues about which approach is preferable. AF prevalence rises with age from 0.4% to 1% in the general population to 11% in those aged >70 years. AF is associated with a pro-thrombotic state and other comorbidities; age, hypertension, heart failure and diabetes mellitus all play a key role in AF pathogenesis. Anti-coagulation is essential for stroke prevention in patients with AF and is recommended for patients with one or more risk factors for stroke. Used within the recommended therapeutic range, warfarin and other vitamin K antagonists decrease the incidence of stroke and mortality in AF patients. Warfarin remains under-used, however, because of the perceived high risk of haemorrhage, narrow therapeutic window and need for regular monitoring. Several novel anti-coagulants show promise in AF-related stroke prevention. In particular, the novel, oral, direct thrombin inhibitor, dabigatran etexilate, recently licensed by the US Food and Drug Administration (FDA) and Health Canada has shown improved efficacy and safety compared with warfarin for stroke prevention in AF, and has the potential to replace warfarin in this indication. The increasing number of new therapeutic options, including improved anti-arrhythmic agents, novel anti-coagulants and more accessible ablation techniques, are likely to deliver better care for AF patients in the near future.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".