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
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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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".