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Record W4406118033 · doi:10.1093/ehjqcco/qcae114

Atrial fibrillation screening and clinical outcomes: a meta-analysis of randomized controlled trials

2025· review· en· W4406118033 on OpenAlexaff
Ulrich Flore Nyaga, Joseph Kamtchum‐Tatuene, Brice Nouthé, Clovis Nkoké, Jean Jacques Noubiap

Bibliographic record

VenueEuropean Heart Journal - Quality of Care and Clinical Outcomes · 2025
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsFraser HealthUniversity of British Columbia
Fundersnot available
KeywordsRandomized controlled trialAtrial fibrillationMeta-analysisMedicineInternal medicineIntensive care medicineCardiology

Abstract

fetched live from OpenAlex

BACKGROUND: Recommendations on atrial fibrillation (AF) screening by various scientific societies are inconsistent due to uncertainty about its benefit. This study aimed to summarize data from randomized controlled trials (RCTs) on the impact of AF screening on thromboembolism, major bleeding, and mortality. METHODS AND RESULTS: We searched PubMed/MEDLINE and Embase to identify studies providing relevant data through 5 September 2024. Risk ratios (RRs) for each reported outcome of interest were pooled through a meta-analysis with random effects models. We included six RCTs reporting data from 74 145 individuals. AF screening was associated with higher AF detection compared with no intervention [RR 2.54, 95% confidence interval (CI): 1.57-4.11, P < 0.001], and more common initiation of oral anticoagulation (RR 2.19, 1.51-3.18, P < 0.001). Incident ischaemic stroke (RR 0.93, 0.87-1.00, P = 0.048) and thromboembolism including ischaemic stroke, transient ischaemic attack, or systemic embolism (0.93, 95% CI: 0.87-0.99, P = 0.026) were less frequent in individuals who underwent AF screening vs. controls. There was no difference for major bleeding, (RR 0.99, 95% CI: 0.93-1.06, P = 0.830), haemorrhagic stroke (RR 0.94, 95% CI: 0.80-1.11, P = 0.497) and all-cause mortality (RR 0.99, 95% CI: 0.95-1.02, P = 0.411). CONCLUSION: AF screening might be beneficial, especially in reducing thromboembolic events.

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.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchMeta-epidemiology (broad)
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Meta-analysishigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.092
metaresearch head score (Gemma)0.158
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMetaresearch, Meta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0920.158
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0770.134
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.574
GPT teacher head0.593
Teacher spread0.019 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

MetaresearchMeta-epidemiology (broad)

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designMeta-analysis
DomainMethods
GenreReview

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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