Bibliographic record
Abstract
Atrial fibrillation (AF) is the tachyarrhythmia of the new millennium. There has been a dramatic increase in research on the management of this problem over the last 10 years. Presently, there is no clear consensus on the most appropriate endpoints to be used in studies of therapy for AF, particularly those concerning rhythm management itself. Endpoints for studies of rhythm management should be based firmly on the objectives of therapy for AF. Some objectives of therapy are obvious, but others, such as reduction of mortality, are not and are somewhat controversial. Clinically relevant endpoints are to be preferred but have been underutilized. Using clinical events as endpoints is complicated by the fact that event rates are low and large sample sizes are needed. Cost and cost-effectiveness are endpoints that are becoming increasingly important but also have been underutilized. Clinical classification of AF is an important factor to be considered in planning studies of AF rhythm management. Patient selection can have a profound effect on the outcome of certain surrogate endpoints. The main limitation of these endpoints is that they assume improvement in the surrogate measurement is closely correlated to a good clinical outcome. In fact, there is ample evidence that such a correlation is quite poor at times. Potential solutions to the problems discussed here include wider appreciation of the problem, use of carefully crafted composite clinical endpoints, and better calibration of surrogate endpoints against clinical endpoints. More research on these issues is urgently needed.
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 | 0.169 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".