The risks and costs of multiple-generic substitution of topiramate
Bibliographic record
Abstract
OBJECTIVE: To investigate clinical and economic consequences following generic substitution of one vs multiple generics of topiramate (Topamax; Ortho-McNeil Neurologics, Titusville, NJ). METHODS: Medical and pharmacy claims data of Régie de l'Assurance-Maladie du Québec from January 2006 to October 2007 were used. Patients with epilepsy treated with topiramate were selected. An open-cohort design was used to classify the observation period into periods of brand, single-generic, and multiple-generic use. One-year generic-switch and switchback-to-brand rates were estimated using Kaplan-Meier methodology. Medical resource utilization and costs were compared among the three periods using multivariate regression analysis. RESULTS: In total, 948 patients were observed during 1,105 person-years of brand use, 233 person-years of single-generic use, and 92 person-years of multiple-generic use. A total of 23% of generic users received at least two different generic versions. Compared to brand use, multiple-generic use was associated with higher utilization of other prescription drugs (incidence rate ratio [IRR] = 1.27, 95% confidence interval [CI] = 1.24-1.31), higher hospitalization rates (0.48 vs 0.83 visit/person-year, IRR = 1.65, 95% CI = 1.28-2.13), and longer hospital stays (2.6 vs 3.9 days/person-year, IRR = 1.43, 95% CI = 1.27-1.60), but the effect was less pronounced in single-generic use (hospitalization: IRR = 1.08, 95% CI = 0.88-1.34, length of stay: IRR = 1.12, 95% CI = 1.03-1.23). The risk of head injury or fracture was nearly three times higher (hazard ratio = 2.84, 95% CI = 1.24-6.48) following a generic-to-generic switch compared to brand use. The total annualized health care cost per patient was higher in the multiple-generic than brand periods by C$1,716 (cost ratio = 1.21, p = 0.0420). CONCLUSION: Multiple-generic substitution of topiramate was significantly associated with negative outcomes, such as hospitalizations and injuries, and increased health care costs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".