0863 Evaluation of sleep apnea among consecutive patients with all patterns of atrial fibrillation
Bibliographic record
Abstract
Abstract Introduction Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice and is associated with significant morbidity, mortality, and financial burden. Obstructive sleep apnea is more common in individuals with AF and may impair AF treatment efficacy including with catheter ablation. However, the prevalence of undiagnosed OSA in all-comers with AF is unknown. Methods This pragmatic, phase IV prospective cohort study will test 250-300 consecutive ambulatory AF patients with all patterns of atrial fibrillation (paroxysmal, persistent, and long-term persistent) and no prior sleep testing for OSA using the WatchPAT system, a home sleep test. Results We report the design, methodology, and results from the initial pilot enrollment of approximately 25% (N=52) of the planned sample size. The primary outcome is the prevalence of undiagnosed OSA in all-comers with AF. Additional outcomes of interest include the sensitivity and specificity of clinical screening instruments for OSA in the AF population and AF-related quality of life measurements among those with and without OSA. Results of the pilot dataset demonstrate a 77.5% prevalence of at least mild (AHI≥5) OSA or greater in all-comers with AF. Conclusion Based on our preliminary dataset representing one quarter of the planned total enrollment size of 250-300, there is a high prevalence of OSA in all-comers with AF. Support (if any) Zoll Medical
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".