Comparative Effectiveness of Generic Atorvastatin and Lipitor <sup>®</sup> in Patients Hospitalized with an Acute Coronary Syndrome
Bibliographic record
Abstract
BACKGROUND: Although generic medications are approved based on bioequivalence with brand-name medications, there remains substantial concern regarding their clinical effectiveness and safety. Lipitor(®), available as generic atorvastatin, is one of the most commonly prescribed statins. Therefore, we compared the effectiveness of generic atorvastatin products and Lipitor(®). METHODS AND RESULTS: We conducted a population-based cohort study, using propensity score matching to minimize potential confounding of patients ≥65 years, discharged alive after acute coronary syndrome (ACS) hospitalization between 2008 and 2012 in Ontario, Canada, who were prescribed Lipitor(®) or generic atorvastatin within 7 days of discharge. The primary outcome was 1-year death/recurrent ACS hospitalization. Secondary outcomes included hospitalization for heart failure, stroke, new-onset diabetes, rhabdomyolysis, and renal failure. In the 7863 propensity-matched pairs (15 726 patients), mean age was 76.9 years, 56.3% were male, 87.6% had myocardial infarction, and all patients had complete follow-up. At 1 year, 17.7% of those prescribed generic atorvastatin and 17.7% of those prescribed Lipitor(®) experienced death or recurrent ACS (hazard ratio, 1.00; 95% CI, 0.93-1.08; P=0.94). No significant differences in rates of secondary outcomes between groups were observed. Prespecified subgroup analyses by age, sex, diabetes, atorvastatin dose, or admission diagnosis found no outcome difference between groups. CONCLUSIONS: Among older adults discharged alive after ACS hospitalization, we found no significant difference in cardiovascular outcomes or serious, infrequent side effects in patients prescribed generic atorvastatin compared with those prescribed Lipitor(®) at 1 year. Our findings support the use of generic atorvastatin in ACS, which could lead to substantial cost saving for patients and health care plans without diminishing population clinical effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".