Research priorities for the study of atrial fibrillation during acute and critical illness: recommendations from the Symposium on Atrial Fibrillation in Acute and Critical Care
Bibliographic record
Abstract
Atrial fibrillation (AF) is a common arrhythmia encountered in acute and critical illness and is associated with poor short and long-term outcomes. Given the consequences of developing AF, research into prevention, prediction and treatment of this arrhythmia in the critically ill are of great potential benefit, however, study of AF in critically ill patients faces unique challenges, leading to a sparse evidence base to guide management in this population. Major obstacles to the study of AF in acute and critical illness include absence of a common definition, challenges in designing studies that capture complex etiology and assess causality, lack of a clear outcome set, difficulites in recruitment in acute environments with respect to timing, consent, and workflow, and failure to embed studies into clinical care platforms and capitalize on emerging technologies. Collaborative effort by researchers, clinicians, and stakeholders should be undertaken to address these challenges, both through interdisciplinary cooperation for the optimization of research efficiency and advocacy to advance the understanding of this common and complex arrhythmia, resulting in improved patient care and outcomes. The Symposium on Atrial Fibrillation in Acute and Critical Care was convened to address some of these challenges and propose potential solutions.
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 | 0.174 | 0.183 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.013 | 0.017 |
| Research integrity | 0.064 | 0.062 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".