Does intensity of rate-control influence outcome in atrial fibrillation? An analysis of pooled data from the RACE and AFFIRM studies
Bibliographic record
Abstract
AIMS: The AFFIRM and RACE studies showed that rate control is an acceptable treatment strategy for atrial fibrillation (AF). We examined whether strict rate control offers benefit over more lenient rate control. METHODS AND RESULTS: We compared the outcome of patients enrolled in the rate-control arms of AFFIRM and RACE, using data from patients who met a composite of overlapping inclusion and exclusion criteria. We evaluated 1091 patients, 874 from AFFIRM and 217 from RACE. In AFFIRM, the rate-control strategy aimed for a resting heart rate < or =80 bpm and heart rate during daily activity of < or =110 bpm. In RACE, a more lenient approach was taken: resting heart rate <100 bpm. Primary endpoint was a composite of mortality, cardiovascular hospitalization, and myocardial infarction. Mean heart rate across all follow-up visits for patients in AF was lower in AFFIRM (76.1 vs. 83.4 bpm). Event-free survival for the occurrence of the primary endpoint did not differ (64% in AFFIRM vs. 66% in RACE). Patients with mean heart rates during AF within the AFFIRM (< or =80) or RACE (<100) criteria had a better outcome than patients with heart rates > or =100 (hazard ratios 0.69 and 0.58, respectively, for < or =80 and <100 compared with > or =100 bpm). CONCLUSION: Stringency of the approach to rate control, based on the comparison of the AFFIRM and RACE studies, was not associated with an important difference in clinical events.
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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 | 0.092 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".