Abstract WP179: Impact Of Different Cardiac Rhythm Monitoring Strategies On Secondary Stroke Prevention: A Systematic Review And Network Meta-analysis Of Randomized Controlled Clinical Trials
Bibliographic record
Abstract
Background and Purpose: Prolonged cardiac rhythm monitoring can reveal a substantial proportion of ischemic stroke (IS) patients with atrial fibrillation (AF). We sought to evaluate the potential utility of available prolonged cardiac rhythm monitoring strategies with respect to secondary stroke prevention. Methods: We searched Medline and Scopus databases to identify randomized controlled clinical trials (RCTs) comparing AF detection, anticoagulation initiation and stroke recurrence rates in patients with history of recent IS or transient ischemic attack (TIA) receiving cardiac rhythm monitoring with implantable loop recorders (ILRs), 30-days external loop recorders or Holter monitors. We performed a network meta-analysis to combine direct and indirect evidence for any given pair of monitoring devices that were evaluated within a trial and reported effect estimates with risk ratios (RRs) and corresponding 95% confidence intervals (95%CIs). Results: We identified 5 RCTs including a total of 2202 patients (mean age 68 years, 40% women). In indirect analyses the likelihood of AF detection and anticoagulation initiation was higher for both ILR (RR=8.48, 95%CI: 3.41, 21.06; RR=3.29, 95%CI: 1.70-6.39) and external loop recorders (RR=3.06, 95%CI: 1.66, 5.61; RR=1.63, 95%CI: 1.03-2.58) compared to Holter devices. The probability of AF detection and anticoagulation initiation was lower for Holter and external loop recorders compared to ILR devices (RR=0.36, 95%CI: 0.15, 0.85 and RR=0.50, 95%CI: 0.25-0.98, respectively). No difference in the risk of stroke recurrence was found in the indirect comparisons of different cardiac rhythm monitoring strategies. Conclusion: The likelihood of AF detection and anticoagulation initiation after an ischemic stroke or TIA is higher with ILRs compared to both external loop recorders and Holter devices.
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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 | 0.025 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".