Cost effectiveness of HMG-CoA reductase inhibition in Canada.
Bibliographic record
Abstract
OBJECTIVE: To assess the cost effectiveness of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor therapy, particularly atorvastatin, in primary and secondary prevention of coronary artery disease (CAD) in Canada. METHODS: A Markov model was developed in which costs and effectiveness of atorvastatin were compared with those of other statins and with no drug therapy in primary and secondary prevention of CAD. PATIENTS: Cost effectiveness was assessed for cohorts of patients with risk profiles defined by CAD status, age, sex, pretreatment low density lipoprotein cholesterol level and presence of sentinel coronary risk factors. Coronary risk was estimated by using initial and subsequent event coronary risk equations from the Framingham Heart Study, and risk factors were estimated by using Canadian population survey data. Recent estimates of the costs of CAD-related medical care in Canada were used to assign costs to health states and acute coronary events. INTERVENTIONS: Interventions included atorvastatin 10 mg, simvastatin 10 mg, pravastatin 20 mg, fluvastatin 20 mg, lovastatin 20 mg and no pharmacological therapy. RESULTS: Incremental cost effectiveness ratios (CDN$/year of life gained) relative to no therapy were lowest for atorvastatin and highest for pravastatin across all risk profiles. Atorvastatin was less costly and more effective than lovastatin, pravastatin and simvastatin in primary and secondary prevention, and conferred additional health benefits at a reduced cost per year of life gained compared with fluvastatin. CONCLUSIONS: Atorvastatin was found to be the most cost effective statin in primary and secondary prevention of CAD.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".