Economic evaluation of increasing population rates of cardiac catheterization
Bibliographic record
Abstract
BACKGROUND: Increasing population rates of cardiac catheterization can lead to the detection of more people with high risk coronary disease and opportunity for subsequent revascularization. However, such a strategy should only be undertaken if it is cost-effective. METHODS: Based on data from a cohort of patients undergoing cardiac catheterization, and efficacy data from clinical trials, we used a Markov model that considered 1) the yield of high-risk cases as the catheterization rate increases, 2) the long-term survival, quality of life and costs for patients with high risk disease, and 3) the impact of revascularization on survival, quality of life and costs. The cost per quality-adjusted life year was calculated overall, and by indication, age, and sex subgroups. RESULTS: Increasing the catheterization rate was associated with a cost per QALY of CAN$26,470. The cost per QALY was most attractive in females with Acute Coronary Syndromes (ACS) ($20,320 per QALY gained), and for ACS patients over 75 years of age ($16,538 per QALY gained). However, there is significant model uncertainty associated with the efficacy of revascularization. CONCLUSION: A strategy of increasing cardiac catheterization rates among eligible patients is associated with a cost per QALY similar to that of other funded interventions. However, there is significant model uncertainty. A decision to increase population rates of catheterization requires consideration of the accompanying opportunity costs, and careful thought towards the most appropriate 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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".