Is Screening for Atrial Fibrillation in Canadian Family Practices Cost-Effective in Patients 65 Years and Older?
Bibliographic record
Abstract
We present an economic evaluation of a recently completed cohort study in which 2054 seniors were screened for atrial fibrillation (AF) in 22 Canadian family practices. Using a Markov model, trial and literature data were used to project long-term outcomes and costs associated with 4 AF screening strategies for individuals aged 65 years or older: no screening, screen with 30-second radial manual pulse check (pulse check), screen with a blood pressure machine with AF detection (BP-AF), and screen with a single-lead electrocardiogram (SL-ECG). Costs and outcomes were discounted at 1.5% and the model used a lifetime horizon from a public payer perspective. Compared with no screening, screening for AF in Canadian family practice offices using pulse check or screen with a blood pressure machine with AF detection is the dominant strategy whereas screening with SL-ECG is a highly cost-effective strategy with an incremental cost per quality-adjusted life-year (QALY) gained of CAD$4788. When different screening strategies were compared, screening with pulse check had the lowest expected costs ($202) and screening with SL-ECG had the highest expected costs ($222). The no-screening arm resulted in the lowest number of QALYs (8.74195) whereas pulse check and SL-ECG resulted in the highest expected QALYs (8.74362). Probabilistic analysis confirmed that pulse check had the highest probability of being cost-effective (63%) assuming a willingness to pay of $50,000 per QALY gained. Screening for AF in seniors during routine appointments with Canadian family physicians is a cost-effective strategy compared with no screening. Screening with a pulse check is likely to be the most cost-effective strategy.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.002 | 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".