Monitoring time-to-detection of recurrent atrial fibrillation in patients with transient new-onset atrial fibrillation detected initially during hospitalization for noncardiac surgery or medical illness
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Approximately one-third-of patients with transient new-onset atrial fibrillation (AF) during hospitalization for noncardiac surgery or medical illness will have recurrent AF within 1 year when assessed using two 14-day ECG monitors. The proportion of patients that would be diagnosed with recurrent AF with less monitoring is unknown. METHODS: We used data from a prospective cohort of participants with transient new-onset AF while hospitalized for noncardiac surgery or medical illness, who wore one or two 14-day ECG monitors. We calculated the proportion of patients that would be diagnosed with recurrent AF with different durations of ECG monitoring and the median time-to-detection of recurrent AF lasting ≥30 s. RESULTS: -VASc 3) wore an ECG monitor a median of 1.5 months following hospital discharge; 83 (59.7 %) wore a second monitor at median of 5.8 months after the first monitor. Recurrent AF was detected in 5.0 % of participants by 1 day, 5.8 % by 2 days, 6.5 % by 3 days, 12.2 % by 7 days, 21.6 % by 14 days and in 28.8 % by the end of the second 14-day monitor. Median monitoring time to recurrent AF was 5.3 (IQR 1.4-9.7) days. CONCLUSIONS: In patients with transient new-onset AF during hospitalization for another reason, the rate of detection of recurrent AF increased with longer monitoring durations. Approximately 80 % of diagnoses were made after 2 days of monitoring; the likelihood of capturing recurrent AF was 4 times higher with 14 days of monitoring compared to 2 days.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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