Bibliographic record
Abstract
Atrial fibrillation (AF) is a major health problem and has been described as one of two emerging cardiovascular epidemics at the turn of the century. It is the most frequent cardiac arrhythmia, affecting 5% of individuals aged over 65 years, and it is associated with an increased risk of stroke and a doubling of mortality. Hypertension has been shown to be the most prevalent, independent, and potentially modifiable risk factor for AF. The current treatment of patients with AF is inadequate. More than 50% of patients have recurrences of the arrhythmia within 12 months despite therapy with potent anti-arrhythmic drugs, thus new strategies to prevent AF are needed. There are several lines of evidence suggesting that renin–angiotensin system (RAS) inhibition may reduce the incidence of new-onset AF as well as AF recurrence. In experimental models, angiotensin-converting enzyme (ACE) inhibitors have been shown to decrease both atrial structural and functional remodelling and AF; these effects appear to be specific to RAS inhibition and to be independent of the haemodynamic benefits of blood pressure control. A recently published meta-analysis of 11 clinical studies suggested that RAS inhibition reduced the risk of AF occurrence by 28%. In a cohort study involving 12 000 patients with hypertension, ACE inhibition was associated with a reduced incidence of AF. The bulk of evidence to date suggests that ACE inhibition may be a novel target to prevent the recurrence of AF in patients with hypertension; however, this has not been tested in an adequately powered prospective trial. The ongoing Canadian Trial on Atrial Fibrillation–2 (CTAF-2) is therefore testing the hypothesis that perindopril, a long-acting ACE inhibitor with a high affinity for tissue ACE, will be effective in preventing recurrences of AF in patients with hypertension.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".