Design and rationale of the atrial fibrillation occurring transiently with stress (AFOTS) follow‐up cohort study
Bibliographic record
Abstract
Atrial fibrillation occurring transiently with stress (AFOTS) describes the first detection of AF in a patient who is hospitalized for a non-cardiac medical illness or following non-cardiac surgery. Uncertainty exists whether episodes of AFOTS are due to reversible precipitants and will not recur after recovery, or if they are paroxysmal atrial fibrillation (AF) that is detected during inpatient cardiac monitoring. Previous studies have used retrospective, non-systematic and ultimately low-sensitivity protocols to investigate the recurrence of AF in patients with AFOTS. The prospective, multi-center, investigator-initiated AFOTS Follow-Up Cohort Study will enroll 138 case patients with AFOTS in the setting of non-cardiac surgery or medical illness, matched 1:1 with control patients for age, sex, stressor, and hospital unit. Participants will wear a 14-day ECG heart monitor at 1 and 6 months after hospital discharge. Over 12 months of follow-up, we will collect data regarding participant's medications, and clinical events. The primary endpoint is detection of 30 or more seconds of AF after hospital discharge. To date, 50% of the target sample has been enrolled. The study is expected to complete enrolment in mid-2019 and conclude 1 year later. The AFOTS follow-up study will employ a systematic protocol to detect AF and will provide a precise and valid estimate of AF recurrence following AFOTS. This study will establish whether patients with AFOTS have an increased propensity to AF after hospitalization as compared to matched controls and may inform the management of this population.
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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 | 0.058 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".