Cost-effectiveness of atrial fibrillation screening in Canadian community practice
Bibliographic record
Abstract
Background: Contemporary guidelines recommend opportunistic screening for atrial fibrillation (AF). Objective: The objective of this study was to assess the cost-effectiveness of single time point opportunistic AF screening for patients 65 years and older by using the single-lead electrocardiogram. Methods: An established Markov cohort model was adapted by updating the background mortality estimates, epidemiology, screening efficacy, treatment patterns, resource use, and cost inputs to reflect a Canadian health care setting. Inputs were derived from a contemporary prospective screening study performed in Canadian primary care settings (screening efficacy and epidemiology) and the published literature (unit costs, epidemiology, mortality, utility, and treatment efficacy). The impact of screening and oral anticoagulant treatment on the cost and clinical outcomes was analyzed. A Canadian payer perspective over lifetime was used for analysis, with costs expressed in 2019 Canadian dollars. Results: Among the estimated screening-eligible population of 2,929,301 patients, the screening cohort identified an additional 127,670 AF cases compared with the usual care cohort. The model estimated avoidance of 12,236 strokes and incremental quality-adjusted life-years of 59,577 (0.02 per patient) over lifetime in the screening cohort. Cost savings were substantial because of improved health outcomes, reflecting screening being the dominant strategy (affordable and effective). Model results were robust across sensitivity and scenario analyses. Conclusion: Single time point opportunistic screening of AF using a single-lead electrocardiogram device in Canadian patients 65 years and older without known AF may provide improved health outcomes with cost savings from the perspective of a single payer health care environment.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".