CANADIAN JOURNAL OF DIABETES. 2009;33(4):363-374. COST-EFFECTIVENESS OF ATORVASTATIN IN DIABETES | 363
Bibliographic record
Abstract
OBJECTIvE: To assess the clinical and economic benefits of atorvastatin for Canadian patients with type 2 diabetes from a Canadian Ministry of Health perspective. METhODS: A Markov cost-effectiveness model based on the clinical outcomes of the Collaborative Atorvastatin Diabetes Study was populated with a hypothetical cohort of patients with type 2 diabetes and no history of cardiovascular (CV) events, receiving 10 mg/day atorvastatin or placebo. Model inputs were retrieved from published literature and public data sets. The time horizon was 5 years, with additional pro-jections for 10 and 25 years. Deterministic and probabilistic sensitivity analyses were performed. RESULTS: Over 5 years, patients treated with atorvastatin experienced fewer CV events, gained 0.02 quality-adjusted life years (QALYs) on a per-patient basis and had 1 % fewer deaths vs. placebo, at an additional cost of $1388 per patient. The incremental cost-effectiveness of atorvasta-tin was $70,773 (95 % CI $33,981–$195,914), $12,687 (95 % CI dominant–$66,048) and $1,362 (95 % CI domi-nant–$49,432) per QALY at 5, 10 and 25 years, respectively. The model was sensitive to variations in hazard ratios for CV events, age, systolic blood pressure and cholesterol levels. CONCLUSIONS: This study supports the cost-effectiveness of atorvastatin for the primary prevention of major CV events in patients with type 2 diabetes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.001 |
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".