Cost‐Effectiveness of Cilostazol Added to Aspirin or Clopidogrel for Secondary Prevention After Noncardioembolic Stroke
Bibliographic record
Abstract
Background The objective of the study was to assess the cost-effectiveness of cilostazol (a selective phosphodiesterase 3 inhibitor) added to aspirin or clopidogrel for secondary stroke prevention in patients with noncardioembolic stroke. Methods and Results A Markov model decision tree was used to examine lifetime costs and quality-adjusted life years (QALYs) of patients with noncardioembolic stroke treated with either aspirin or clopidogrel or with additional cilostazol 100 mg twice daily. Cohorts were followed until all patients died from competing risks or ischemic or hemorrhagic stroke. Probabilistic sensitivity analysis using Monte Carlo simulation was used to model 10 000 cohorts of 10 000 patients. The addition of cilostazol to aspirin or clopidogrel is strongly cost saving. In all 10 000 simulations, the cilostazol strategy resulted in lower health care costs compared with aspirin or clopidogrel alone (mean $13 488 cost savings per patient; SD, $8087) and resulted in higher QALYs (mean, 0.585 more QALYs per patient lifetime; SD, 0.290). This result remained robust across a variety of sensitivity analyses, varying cost inputs, and treatment effects. At a willingness-to-pay threshold of $50 000/QALY, average net monetary benefit from the addition of cilostazol was $42 743 per patient over their lifetime. Conclusions Based on the best available data, the addition of cilostazol to aspirin or clopidogrel for secondary prevention following noncardioembolic stroke results in significantly reduced health care costs and a gain in lifetime QALYs.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".