Screening for atrial fibrillation to prevent stroke: a meta-analysis
Bibliographic record
Abstract
Abstract Aims We aimed to summarize existing evidence from published randomized trials that assessed atrial fibrillation (AF) screening for stroke prevention. Methods and results We searched MEDLINE for randomized trials that enrolled patients without known AF, screened for AF using electrocardiogram-based methods, and reported stroke outcomes. For this analysis, we excluded studies that focused on post-stroke populations. We combined data using a random-effects model and performed trial sequential meta-analysis using an O’Brien-Fleming alpha-spending function. We identified four randomized clinical trials with a total of 35 836 participants. The populations, screening intervention, and definition of stroke varied markedly. As compared with no screening, AF screening was associated with a reduction in stroke (relative risk 0.91; 95% confidence interval: 0.84–0.99]. Trial sequential meta-analysis found that the cumulative z-score did not cross the stopping boundary. After polling members of the AF-SCREEN and AFFECT-EU consortia, we identified a further 12 trials that are complete but have not yet reported stroke outcomes or are ongoing and expected to collect stroke outcomes. These consortia are planning an individual participant data meta-analysis which will permit the exploration of methodological heterogeneity. Conclusions If and how to screen for AF is an important public health concern. The body of evidence published to date suggests that AF could be effective to prevent strokes in some settings. The AF-SCREEN/AFFECT-EU individual patient data meta-analysis aims to comprehensively assess the benefits and risks of AF screening, and determine how population, screening method, and health-system factors influence stroke prevention.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.073 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".