The Changing Landscape in Oral Anticoagulation – The Last Pieces of the Puzzle
Bibliographic record
Abstract
][3] The management of AF has seen marked changes in recent years with the availability of new anticoagulants, antiarrhythmic drugs, and the wider availability of catheter ablation. 35][6][7] Although most of the recommendations in the guidelines for the management of AF are based on sound evidence, they are not totally consistent 8 and are not always fully implemented into practice. 9-11Therefore, it is important to ascertain how these guidelines are being translated into practice.This is the purpose of the registries that have flourished in recent years.The focus of the registry is to address the current situation pertaining to a particular medical condition and predict how it may change.The Prevention of Thromboembolic Events -European Registry (PREFER) in AF is a multinational, multi-centre, prospective disease registry with the objective of gaining a detailed insight into the characteristics and management of patients with AF.The main focus is the prevention of thromboembolic events.Subjects have completed a baseline visit and will receive a follow-up visit 12 months after baseline.This will provide the opportunity to monitor the changes that occur in the pattern of treatment of AF within a year, at a time of rapid changes in the anticoagulation landscape in Europe.The registry is based in several European countries including Austria, France, Germany, Italy, Spain Switzerland, and the United Kingdom.For regional comparisons, Austria, Switzerland and Germany were combined into one pre-specified region.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 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".