Abstract WP229: Prolonged Monitoring of Cardiac Rhythm With Wireless Cardiophone for Real Time Detection of Atrial Fibrillation After a Cerebral Ischemic Event (PEAACE II Study) The Edmonton Alberta Experience
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) is a leading cause of preventable stroke and can be prevented with anticoagulants. Prolonged cardiac monitoring can lead to better detection of AF. Real time cardiac monitoring may lead to earlier diagnosis and treatment. Objective: Primary objective is to detect AF using wireless cardiophone for 14 days in stroke patients. Secondary objective is to determine the reduction of time of AF diagnosis resulting in a prompt change in clinical management. Method: This was a Cohort/Prospective study at the Univesity of Alberta Hospital. Patients ≥40 years of age (without known AF on ECG/Holter) who had an ischemic stroke or TIA within ≤90 days were eligible. The signals from cardiophone were analyzed in real time at Canadian cardiac center in Windsor, Ontario. The incidence and time of onset of AF and change in medical treatment (anti-coagulation) was recorded. Results: Out of 120 patients, 118 completed monitoring for more than 48 hours. Twenty out of these 118 subjects (≈ 17 %) were shown positive for AF. When compared with the incidence of 5% reported in historical controls wearing 24 hour holter, the difference was significant (chi square p=0.004). Fourteen out of 20 AF subjects (70%)had AF duration of < 30 seconds, while 6/20 (30%) had a duration of >30 seconds. Mean time from onset of arrhythmia to report was ≈48 hours; shorter than the time for report with Holter (≈10 days in Alberta). Ninety percent of AF positive subjects were started on anti-coagulation therapies. Conclusion: Prolonged cardiac monitoring for 14 days increased the detection of AF approximately 3 fold as compared to 24 hour Holter. Cardiophone device allowed feasible real time monitoring of heart rhythm and faster reporting time (within 48 hours) that lead to prompt change in medical treatment by the health care physician.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".