High-Sensitivity Estimate of the Incidence of New-Onset Atrial Fibrillation in Critically Ill Patients
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Bibliographic record
Abstract
To estimate the incidence of new-onset atrial fibrillation in critically ill patients. DESIGN: Prospective cohort. SETTING: Medical-surgical ICU. SUBJECTS: Consecutive patients without a history of atrial fibrillation but with atrial fibrillation risk factors. INTERVENTIONS: Electrocardiogram patch monitor until discharge from hospital or up to 14 days. MEASUREMENTS AND MAIN RESULTS: A total of 249 participants (median age of 71 yr [interquartile range] 64-78 yr; 35% female) completed the study protocol of which 158 (64%) were admitted to ICU for medical illness, 78 (31%) following noncardiac surgery, and 13 (5%) with trauma. Median Acute Physiology and Chronic Health Evaluation II score was 16 (interquartile range, 12-22). Median duration of patch electrocardiogram monitoring, ICU, and hospital lengths of stay were 6 (interquartile range, 3-12), 4 (interquartile range, 2-8), and 11 days (interquartile range, 5-23 d), respectively.Atrial fibrillation ≥ 30 seconds was detected by the patch in 44 participants (17.7%), and three participants (1.2%) had atrial fibrillation detected clinically after patch removal, resulting in an overall atrial fibrillation incidence of 18.9% (95% CI, 14.2-24.3%).Total duration of atrial fibrillation ranged from 53 seconds to the entire monitoring time. The proportion of participants with ≥1 episode(s) of ≥6 minute, ≥1 hour, ≥12 hour and ≥24 hour duration was 14.8%, 13.2%, 7.0%, and 5.3%, respectively. The clinical team recognized only 70% of atrial fibrillation cases that were detected by the electrocardiogram patch. CONCLUSIONS: Among patients admitted to an ICU, the incidence of new-onset atrial fibrillation is approximately one in five, although approximately one-third of cases are not recognized by the clinical team.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it