The SilenT AtRial FIBrillation (STAR-FIB) study programme – design and rationale
Bibliographic record
Abstract
AIMS OF THE STUDY: Anticoagulation of patients with screen-detected atrial fibrillation may prevent ischaemic strokes. The STAR-FIB study programme aims to determine the age- and sex-specific prevalence of silent atrial fibrillation and to develop a clinical prediction model to identify patients at risk of undiagnosed atrial fibrillation in a hospitalised patient population. METHODS: The STAR-FIB study programme includes a prospective cohort study and a case-control study of hospitalised patients aged 65–84 years, evenly distributed for both age and sex. We recruited 795 patients without atrial fibrillation for the cohort study (49.2% females; median age 74.8 years). All patients had three serial 7-day Holter ECGs to screen for silent atrial fibrillation. The primary endpoint will be any episode of atrial fibrillation or atrial flutter of ≥30 seconds duration. The age- and sex-specific prevalence of newly diagnosed atrial fibrillation will be estimated. For the case-control study, 120 patients with paroxysmal atrial fibrillation were recruited as cases (41.7% females; median age 74.6 years); controls will be randomly selected from the cohort study in a 2:1 ratio. All participants in the cohort study and all cases were prospectively evaluated including clinical, laboratory, echocardiographic and electrical parameters. A clinical prediction model for undiagnosed atrial fibrillation will be derived in the case-control study and externally validated in the cohort study. CONCLUSIONS: The STAR-FIB study programme will estimate the age- and sex-specific prevalence of silent atrial fibrillation in a hospitalised patient population, and develop and validate a clinical prediction model to identify patients at risk of silent atrial fibrillation.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".