The more you look, the more you find
Bibliographic record
Abstract
PURPOSE OF REVIEW: We provide an updated review on the incidence of postoperative atrial fibrillation (POAF) after cardiac surgery as determined by enhanced cardiac rhythm monitoring technology and provide a rationale for why a more aggressive detection approach for POAF may be clinically useful. RECENT FINDINGS: Most of the published literature had focused on the in-hospital incidence of POAF after cardiac surgery. However, recent studies using continuous cardiac rhythm technologies revealed that the incidence of POAF during the postdischarge, subacute (<1 month) phase could be as high as 28%. This is a clinically relevant finding since that POAF is linked with occurrence of future, 'late' atrial fibrillation, and adverse clinical outcomes even beyond 1 year after cardiac surgery. Furthermore, the role of oral anticoagulation is still not well established for cardiac surgical patients with POAF because of lack of randomized trials specifically designed for this patient population. SUMMARY: Emerging data suggest that POAF after cardiac surgery is not a transient, self-resolving phenomenon. Rather, its occurrence is associated with future risk of atrial fibrillation and long-term adverse outcomes such as stroke and death. This highlights the potential importance of enhanced cardiac rhythm monitoring to refine prognostic stratification in this high-risk patient population.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".