Implantable Cardiac Monitoring in the Secondary Prevention of Cryptogenic Stroke
Bibliographic record
Abstract
OBJECTIVE: In this study, we sought to evaluate the impact of implantable cardiac monitoring (ICM) in the prevention of stroke recurrence after a cryptogenic ischemic stroke or transient ischemic attack (TIA). METHODS: We evaluated consecutive patients with cryptogenic ischemic stroke or TIA admitted in a comprehensive stroke center during an 8-year period. We compared the baseline characteristics and outcomes between patients receiving conventional cardiac monitoring with repeated 24-hour Holter-monitoring during the first 5 years in the outpatient setting and those receiving continuous cardiac monitoring with ICM during the last 3 years. Associations on the outcomes of interest were further assessed in multivariable regression models adjusting for potential confounders. RESULTS: We identified a total of 373 patients receiving conventional cardiac monitoring and 123 patients receiving ICM. Paroxysmal atrial fibrillation (PAF) detection was higher in the ICM cohort compared to the conventional cardiac monitoring cohort (21.1% vs 7.5%, p < 0.001). ICM was independently associated with an increased likelihood of PAF detection during follow-up (hazard ratio [HR] = 1.94, 95% confidence interval [CI] = 1.16-3.24) in multivariable analyses. Patients receiving ICM were also found to have significantly higher rates of anticoagulation initiation (18.7% vs 6.4%, p < 0.001) and lower risk of stroke recurrence (4.1% vs 11.8%, p = 0.013). ICM was independently associated with a lower risk of stroke recurrence during follow-up (HR = 0.32, 95% CI = 0.11-0.90) in multivariable analyses. INTERPRETATION: ICM appears to be independently associated with a higher likelihood of PAF detection and anticoagulation initiation after a cryptogenic ischemic stroke or TIA. ICM was also independently related to lower risk of stroke recurrence in our cryptogenic stroke / TIA cohort. ANN NEUROL 2020;88:946-955.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".