[Is long-term use of clopidogrel cost-effective in high-risk atherothrombotic patients?].
Bibliographic record
Abstract
The objective of the analysis is to estimate the long-term costs and effects of clopidogrel versus aspirin in the secondary prevention of ischemic events in patients with a history of more than one atherothrombotic event in Belgium. The following high-risk subpopulations within the CAPRIE trial were analysed: patients with a history of myocardial infarction (MI) or ischemic stroke (IS) prior to the event qualifying for enrolment and patients with prior multiple vascular territory involvement. A Markov model that combined clinical, epidemiological and cost data was used. The base case scenario was based on a treatment duration of 5 years reflecting the long-term use of clopidogrel. A lifelong time horizon was taken, by applying life expectancy data based on the Saskatchewan database. Belgian cost estimates were derived from publicly available sources and literature. Long-term clopidogrel treatment compared to aspirin in patients with a history of MI or IS prior to the event qualifying for enrolment is associated with an incremental cost-effectiveness ratio (ICER) ranging between 2.730 Euro per life year gained (LYG) for the first year of treatment and 8.000 Euro/LYG for a treatment duration of 5 years. In patients with prior multiple vascular territory involvement the ICER of clopidogrel compared to aspirin lays between 3.110 Euro and 5.750 Euro/LYG for the respective treatment durations. Use of clopidogrel for the prevention of subsequent cardiovascular events in patients with a history of more than one ischemic event is associated with favourable ICERs, independently of the presumed treatment duration. Sensitivity analyses varying life expectancy, efficacy of clopidogrel, costs of events and cost of adverse events and discount rates demonstrated the robustness of the results.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".