Monitoring for Atrial Fibrillation in Discharged Stroke and Transient Ischemic Attack Patients: Recommendations
Bibliographic record
Abstract
An ischemic stroke is caused by thrombosis of the cerebral vessels or by emboli from a proximal arterial source or the heart. This blockage deprives the brain cells of vital oxygen and nutrients leading to cell death. A transient ischemic attack (TIA) is a neurological deficit lasting less than 24 hours, caused by cerebral ischemia.Atrial fibrillation (AF) is a type of cardiac arrhythmia, which causes pooling of blood that leads to thrombosis formation and may cause a stroke or TIA. Patients with AF but no history of stroke have a stroke risk of 4.5% per year; however, anticoagulation therapy, can reduce this risk to 1.4% per year. Often patients with AF will not have any symptoms, and therefore they are difficult to identify. Roughly 30% to 40% of first-time ischemic strokes are due to an unknown cause, and are referred to as an embolic stroke of undetermined source (ESUS). Patients who have experienced ESUS may have undiagnosed, or occult, AF. Determining whether they do have AF can be important to help prevent future strokes or TIAs.Long-term electrocardiography (ECG) monitoring using outpatient cardiac monitoring devices can identify occult AF that is undetectable by other means. To this end, outpatient cardiac monitoring devices providing increased mobility for patients and the ability to transmit data wirelessly have been developed, and allow for longer-term surveillance outside the hospital setting. These devices include ambulatory Holter monitors, external loop recorders (ELRs), mobile cardiac outpatient telemetry (MCOT) devices, and implantable loop recorders (ILRs).CADTH conducted a health technology assessment (HTA) on the clinical effectiveness and cost-effectiveness of cardiac monitoring devices in patients discharged from hospital following a stroke or TIA, to help inform decisions about these devices. Patient perspectives and experiences regarding the value and impact of outpatient AF cardiac monitoring devices were also considered.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".