Clinical Equivalence of Generic and Brand-Name Drugs Used in Cardiovascular Disease
Bibliographic record
Abstract
CONTEXT: Use of generic drugs, which are bioequivalent to brand-name drugs, can help contain prescription drug spending. However, there is concern among patients and physicians that brand-name drugs may be clinically superior to generic drugs. OBJECTIVES: To summarize clinical evidence comparing generic and brand-name drugs used in cardiovascular disease and to assess the perspectives of editorialists on this issue. DATA SOURCES: Systematic searches of peer-reviewed publications in MEDLINE, EMBASE, and International Pharmaceutical Abstracts from January 1984 to August 2008. STUDY SELECTION: Studies compared generic and brand-name cardiovascular drugs using clinical efficacy and safety end points. We separately identified editorials addressing generic substitution. DATA EXTRACTION: We extracted variables related to the study design, setting, participants, clinical end points, and funding. Methodological quality of the trials was assessed by Jadad and Newcastle-Ottawa scores, and a meta-analysis was performed to determine an aggregate effect size. For editorials, we categorized authors' positions on generic substitution as negative, positive, or neutral. RESULTS: We identified 47 articles covering 9 subclasses of cardiovascular medications, of which 38 (81%) were randomized controlled trials (RCTs). Clinical equivalence was noted in 7 of 7 RCTs (100%) of beta-blockers, 10 of 11 RCTs (91%) of diuretics, 5 of 7 RCTs (71%) of calcium channel blockers, 3 of 3 RCTs (100%) of antiplatelet agents, 2 of 2 RCTs (100%) of statins, 1 of 1 RCT (100%) of angiotensin-converting enzyme inhibitors, and 1 of 1 RCT (100%) of alpha-blockers. Among narrow therapeutic index drugs, clinical equivalence was reported in 1 of 1 RCT (100%) of class 1 antiarrhythmic agents and 5 of 5 RCTs (100%) of warfarin. Aggregate effect size (n = 837) was -0.03 (95% confidence interval, -0.15 to 0.08), indicating no evidence of superiority of brand-name to generic drugs. Among 43 editorials, 23 (53%) expressed a negative view of generic drug substitution. CONCLUSIONS: Whereas evidence does not support the notion that brand-name drugs used in cardiovascular disease are superior to generic drugs, a substantial number of editorials counsel against the interchangeability of generic drugs.
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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 | 0.125 | 0.499 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".