Prolonged cardiac monitoring for stroke prevention: A systematic review and meta-analysis of randomized-controlled clinical trials
Bibliographic record
Abstract
Abstract Introduction: Prolonged cardiac monitoring (PCM) substantially improves the detection of subclinical atrial fibrillation (AF) among patients with history of ischemic stroke (IS), leading to prompt initiation of anticoagulants. However, whether PCM may lead to IS prevention remains equivocal. Patients and methods: In this systematic review and meta-analysis, randomized-controlled clinical trials (RCTs) reporting IS rates among patients with known cardiovascular risk factors, including but not limited to history of IS, who received PCM for more than 7 days versus more conservative cardiac rhythm monitoring methods were pooled. Results: Seven RCTs were included comprising a total of 9048 patients with at least one known cardiovascular risk factor that underwent cardiac rhythm monitoring. PCM was associated with reduction of IS occurrence compared to conventional monitoring (Risk Ratio: 0.76; 95% CI: 0.59–0.96; I 2 = 0%). This association was also significant in the subgroup of RCTs investigating implantable cardiac monitoring (Risk Ratio: 0.75; 95% CI: 0.58–0.97; I 2 = 0%). However, when RCTs assessing PCM in both primary and secondary prevention settings were excluded or when RCTs investigating PCM with a duration of 7 days or less were included, the association between PCM and reduction of IS did not retain its statistical significance. Regarding the secondary outcomes, PCM was related to higher likelihood for AF detection and anticoagulant initiation. No association was documented between PCM and IS/transient ischemic attack occurrence, all-cause mortality, intracranial hemorrhage, or major bleeding. Conclusion: PCM may represent an effective stroke prevention strategy in selected patients. Additional RCTs are warranted to validate the robustness of the reported associations.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".