Cost effectiveness of rimonabant use in patients at increased cardiometabolic risk: estimates from a Markov model
Bibliographic record
Abstract
SummaryRimonabant, the first selective CB-1 receptor blocker, is expected to reduce cardiometabolic risk substantially. This study assesses the economics of such treatment in patients at elevated cardiometabolic risk.A Markov model was developed using data from the Rimonabant in Obesity (RIO) trial, published risk equations, and UK cost and utility data. Patients begin either in a diabetic or a non-diabetic state and can transition to cardiovascular disease or to death (based on UK life tables). Transitions to diabetes and subsequent cardiovascular events are also counted. Resource use due to events and long-term management were translated to UK costs (2005 GBP). Tariffs for events and states were applied to age-dependent utilities. Extensive univariate and multivariate probabilistic sensitivity analyses were carried out.Over 10 years, 8% will suffer a cardiovascular event with a loss of more than 1,000 quality-adjusted life years (QALYs) and a cost of more than £500,000 per 1,000 patients. Projecting risk for a lifetime, 1 year of rimonabant use is estimated to gain >65 QALYs at £8,574/QALY. In probabilistic sensitivity analysis, incremental cost-effectiveness ratios varied from £2,657 to £22,141/QALY.Based on the metabolic effects seen in clinical trials, rimonabant should reduce cardiovascular risk in obese or overweight people at reasonable cost.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".