<p>Watch-PAT is Useful in the Diagnosis of Sleep Apnea in Patients with Atrial Fibrillation</p>
Bibliographic record
Abstract
BACKGROUND: Early diagnosis and treatment of sleep apnea in patients with atrial fibrillation (AF) is critical. The WatchPAT (WP) device was shown to be accurate for the diagnosis of sleep apnea; however, studies using the WatchPAT device have thus far excluded patients with arrhythmias due to the potential effect of arrhythmias on the peripheral arterial tonometry (PAT) amplitude and pulse rate changes. PURPOSE: To examine the accuracy of the WP in detecting sleep apnea in patients with AF. PATIENTS AND METHODS: Patients with AF underwent simultaneous WP and PSG studies in 11 sleep centers. PSG scoring was blinded to the automatically analyzed WP data. RESULTS: A total of 101 patients with AF (70 males) were recruited. Forty-six had AF episodes during the overnight sleep study. A significant correlation was found between the PSG-derived AHI and the WP- derived AHI (r=0.80, p<0.0001). There was a good agreement between PSG-derived AHI and WP-derived AHI (mean difference of AHI: -0.02±13.2). Using a threshold of AHI ≥15 per hour of sleep, the sensitivity and specificity of the WP were 0.88 and 0.63, respectively. The overall accuracy in sleep staging between WP and PSG was 62% with Kappa agreement of 0.42. CONCLUSION: WP can detect sleep apnea events in patients with AF. AF should not be an exclusion criterion for using the device. This finding may be of even greater importance in the era of the COVID19 epidemic, when sleep labs were closed and most studies were home based.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".