Cost-effectiveness of population screening for aortic stenosis
Bibliographic record
Abstract
AIMS: Aortic stenosis (AS) is a progressive disease predominantly affecting elderly patients that carries significant morbidity and mortality without aortic valve replacement, the only proven treatment. Our objective was to determine the cost-effectiveness of AS screening using transthoracic echocardiography (TTE) in a geriatric population from the perspective of the publicly funded healthcare system in Canada. METHODS AND RESULTS: Markov models estimating the cost-effectiveness ratio (ICER) for AS screening with a one-time TTE were developed. The model included diagnosed and undiagnosed AS health states, hospitalizations, transcatheter aortic valve replacement (TAVR), and post-TAVR health states. Primary analysis included screening at 70 and 80 years of age with intervention at symptom onset, with scenario analysis included for early intervention at the time of severe asymptomatic AS diagnosis. Monte Carlo simulation of 5000 replications was completed with a lifetime horizon and a 1.5% discount for costs and outcomes.Screening for AS at the age of 70 years was associated with an ICER of $156 722, and screening at 80 years of age was associated with an ICER of $28 005, suggesting that screening at 80 years of age is cost-effective when willingness-to-pay per QALY is $50 000. Scenario analysis with early intervention was not cost-effective, with an ICER of $142 157 at 70 years and $124 651 at 80 years. CONCLUSION: Screening for AS at 80 years of age with a one-time TTE, in a Canadian population, improves quality of life and is cost-effective in a publicly funded healthcare system providing, TAVR is reserved for symptomatic patients.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".