Early Detection of Atrial Fibrillation-Atrial Flutter Using Remote Patient Monitoring
Bibliographic record
Abstract
Remote patient monitoring (RPM), patient-reported outcomes (PROs), and biochemical biomarker monitoring (Mitra<sup>®</sup> devices) may be useful for early detection of major adverse cardiac events (MACE). This case report presents a patient enrolled in a precision medicine study in which RPM detected the presence of atrial fibrillation-atrial flutter (AFib-Flutter), facilitating prompt treatment. A 64-year-old male with a history of ischemic heart disease (IHD) initiated his AliveCor Kardia after noting angina which reported “possible atrial fibrillation”. Upon evaluation, AFib-Flutter was confirmed by 12-lead ECG and successfully treated. RPM was recorded continuously through (Fitbit Charge 2), weekly single-channel electrocardiogram rhythm stripe (AliveCor Kardia), PROs through weekly questionnaires, and Mitra<sup>®</sup> devices through monthly fingerpricks. The case report highlights a successful case of detecting AFib-Flutter, expediting treatment and preventing MACE. Precision medicine using RPM may be useful for detecting AFib-Flutter and improving IHD outcomes. Further research is needed. J Med Cases. 2019;10(2):31-36 doi: https://doi.org/10.14740/jmc3151w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".