Fact‐finding survey of antithrombotic treatment for prevention of cerebral and systemic thromboembolism in patients with non‐valvular atrial fibrillation in 9 countries of the Asia‐Pacific region
Bibliographic record
Abstract
Abstract Atrial fibrillation (AF) has been gaining much attention as one of the major causes of cerebral infarction. It is imperative to establish antithrombotic treatment for AF patients. Thus far, guidelines for antithrombotic treatment in the management of AF patients, including the verification of the efficacy of direct thrombin and factor Xa inhibitors, have been published from the United States, Europe, Canada, and Japan. When we look at the Asia‐Pacific region, antithrombotic treatment has not yet been defined, and no such guidelines have been published in this regard. The Asia‐Pacific Heart Rhythm Society (APHRS) conducted a Web‐based survey between June and August 2011, to elucidate the current status of antithrombotic treatment in 9 countries. A total of 363 cardiologists in 9 countries examined 300 patients with cardiovascular disease per month on an average; of these patients, 37 (12%) had nonvalvular AF (NVAF; 6.5% in India to 16.9% in Australia). The survey revealed that NVAF patients were not always administered appropriate antithrombotic treatment. These data give us a foothold for the next step, i.e., the formulation, of the APHRS practice guidelines.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".