Comparison of Generic Drug Reviews for Marketing Authorization between Japan and Canada
Bibliographic record
Abstract
PURPOSE: Generic drugs are assuming an increasingly important role in sustaining modern healthcare systems, as the cost of healthcare, including drug usage, is gradually expanding around the world. To date, published articles comparing generic drug reviews between different countries are scarce. OBJECTIVE: The objective of this study was to examine generic drug reviews in Japan and Canada. METHODS: We surveyed generic drug reviews from Japan and Canada and compared the following points: general matter (application types, type of partial change or Supplement to an Abbreviated New Drug Submission, application and approval numbers, review period, application format, review report, responsibility for review), bioequivalence studies for solid oral dosage forms, and bioequivalence guidelines, guidance, or basic principles regarding various dosage forms. RESULTS: This survey described the many similarities and differences in generic drug reviews between the two countries and points that should be improved to promote better generic drug reviews. In particular, regulations for the definition of the same or different active pharmaceutical ingredients (APIs) are similar for both authorities. CONCLUSIONS: The results clarified the future challenges of generic drug reviews, and the differences highlighted by this survey will be important considerations for the future. This is the first article to present and discuss the details of generic drug reviews between Japan and Canada.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".