Abstract P373: Are Generic Drugs Used in Cardiology as Effective and Safe as Their Brand-name Counterparts? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Generic drugs constitute lower cost alternatives when the original drug patent expires. Two large systematic reviews and meta-analyses (2008; 2016) concluded to similarity in outcomes between brand-name and generic drugs in cardiology. These meta-analyses were possibly biased by the inclusion of ≥ 50% comparative bioavailability studies, not designed nor powered to detect a difference in efficacy or safety between drug types. We aimed to summarize best evidences regarding the effectiveness and safety of generic versus brand-name drugs used in cardiology. Methods: To conduct this systematic review of the literature, scientific databases (MEDLINE and EMBASE) were searched from the latest date among their inception or January 1984, to March 2018. Original research reports comparing the clinical impact of brand-name vs. generic cardiovascular drugs on humans treated in real-life setting were included. Meta-Analyses and subgroup analyses were performed. Heterogeneity (I 2 ) and risk of bias were tested. Results: Among the 3,148 screened abstracts, 72 met inclusion criteria. The studies included a total of 1,048,499 patients who used generic or brand-name cardiovascular drugs. Mean age was 65 +/- 10 years old and 42% were women. The most commonly studied therapeutic classes were antiplatelets, statins and anticoagulants. Platelet function, international normalized ratio and systolic blood pressure were similar in generic and brand-name groups. The risk of bias was modest to serious in the majority of studies. The overall relative risk of all-cause hospitalizations was 1.14 (95% Confidence interval: 1.06 to 1.23; I 2 : 98%), while it was 1.05 (0.98 to 1.14; I 2 : 68%) for cardiovascular hospitalizations (both pooled: 1.10 [1.04 to 1.15]; I 2 : 93%). Conclusion: The risk of hospitalization seemed higher for patients exposed to generics. However, evidences are insufficient and too heterogeneous to conclude that generics are as effective and safe as brand-name drugs in cardiology.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".