Prolonged Cardiac Rhythm Monitoring and Secondary Stroke Prevention in Patients With Cryptogenic Cerebral Ischemia
Bibliographic record
Abstract
Background and Purpose— Although prolonged cardiac rhythm monitoring (PCM) can reveal a substantial proportion of ischemic stroke (IS) patients with atrial fibrillation not detected by conventional short-term monitoring, current guidelines indicate an uncertain clinical benefit for PCM. We evaluated the impact of PCM on secondary stroke prevention using data from available to date randomized clinical trials and observational studies. Methods— We performed a comprehensive literature search in MEDLINE, SCOPUS, CENTRAL (Cochrane Central Register of Controlled Trial), and conference proceedings to identify studies reporting stroke recurrence rates in patients with history of cryptogenic IS or transient ischemic attack (TIA) receiving PCM compared with patients receiving conventional (non-PCM) cardiac monitoring. Results— We included 4 studies (2 randomized clinical trials and 2 observational studies), including a total of 1102 patients (mean age: 68 years, 41% women). We documented an increased incidence of atrial fibrillation detection (risk ratio=2.46; 95% CI, 1.61–3.76) and anticoagulant initiation (risk ratio=2.07; 95% CI, 1.36–3.17) and decreased risk of recurrent stroke (risk ratio=0.45; 95% CI, 0.21–0.97) and recurrent stroke/TIA (risk ratio=0.49; 95% CI, 0.30–0.81) during follow-up for IS/TIA patients who underwent PCM compared with IS/TIA patients receiving conventional cardiac monitoring. In the subgroup analysis, according to study type, atrial fibrillation detection, anticoagulant initiation, and IS/TIA recurrence rates were comparable between PCM and non-PCM in randomized clinical trials and observational studies. No evidence of heterogeneity ( I 2 <12%) was documented across all the aforementioned subgroups. Conclusions— We provide preliminary evidence for a potential impact of PCM on secondary stroke prevention, as patients with cryptogenic IS/TIA undergoing PCM had higher rates of atrial fibrillation detection, anticoagulant initiation, and lower stroke recurrence.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".