Generic Substitution for Brand Name Antiepileptic Drugs: A Survey
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: There are presently 26 different generic preparations for five brand name antiepileptic drugs (AEDs) on the Canadian market with others likely to be released in the near future. The purpose of this review is to examine the basis for the controversy surrounding generic substitution for brand name antiepileptic drugs, to present the results of a survey of neurologists' and patients' attitudes toward generic substitution and to increase neurologists' awareness of the issues. METHODS: The current federal and provincial regulations pertaining to generic drug approval and substitution are reviewed. Published anecdotal and survey reports of the effectiveness and tolerability of generic substitution for AEDs are reviewed. A pilot questionnaire survey of 83 patients from four adult epilepsy clinics and 46 neurologists from across Canada was undertaken to determine attitudes toward generic substitution. RESULTS AND CONCLUSIONS: Several authors have suggested that some AEDs, particularly those with a narrow therapeutic index, may pose problems with generic substitution. Although generic AEDs are lower in price, possible increased side effects and morbidity and the need for closer monitoring could partially offset the cost savings. The results of our survey highlight significant unawareness of the process of generic substitution among both patients and neurologists and reveal a general level of discomfort among neurologists to prescribe generic AEDs. Further data should be obtained about the potential consequences of generic substitution in epilepsy patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".