Screening for atrial fibrillation to prevent stroke: a meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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