Efficacy and cost of HMG-CoA reductase inhibitors in the treatment of patients with primary hyperlipidemia.
Bibliographic record
Abstract
BACKGROUND: Screening for hyperlipidemia is a substantial cost burden, as is its treatment. The choice of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins) and the dose level may have significant implications for both efficient and cost effective therapy. OBJECTIVE: To compare the efficiency and cost of statins. MATERIALS AND METHODS: A meta-analysis was conducted of randomized, controlled trials of monotherapy with fixed doses of statins published in the literature until June 1998. Two authors independently extracted data from 49 trials comprising 14,130 patients. The percentage reduction (95% confidence intervals) of low density lipoprotein (LDL) cholesterol levels was calculated using a random-effects model. Cost efficiency was defined as the percentage decline of LDL cholesterol per dollar of drug cost. RESULTS: The population evaluated had a mean baseline LDL cholesterol concentration of 5.31 mmol/L, a mean age of 53.5 years and a mean 59% proportion of males. In reducing LDL cholesterol concentrations to less than 25% of the baseline concentration, a significantly higher cost efficiency was achieved with simvastatin 2.5 mg (-53.3%/dollar). By targeting a reduction between 25% and 29%, significantly higher cost efficiencies were found with simvastatin 5 mg (-28.9%/dollar), cerivastatin 0.2 mg (-23.8%/dollar) and fluvastatin 40 mg (-23.3%/dollar). For reductions in LDL cholesterol concentrations of 30% to 34%, statistically higher cost efficiencies were achieved with simvastatin 20 mg (-15.0%/dollar) and pravastatin 40 mg (-14. 4%/dollar). Finally, atorvastatin 10 mg yielded a value of -22. 9%/dollar for a 36% reduction in LDL cholesterol concentration. CONCLUSIONS: At current prices of the various doses of statins, the cost efficiency of standard and more aggressive therapies varies substantially. In the context of health care budgets, targeting at-risk patients and using statins judiciously should facilitate the efforts of clinicians and patients to reduce lipid profiles optimally and decrease the cost burden.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".