Bioequivalence of Generic Drugs Commercialised on the Canadian Market
Bibliographic record
Abstract
Several international studies have revealed that there are deficiencies and non bioequivalencies in generic drug reports. The purpose of this study is to determine if monographs were available in both of Canada’s official languages for all generics introduced in the Canadian province of Quebec in 2012 and 2013, if the monographs contained all the required 90% confidence interval for the ratios test/reference of the bioequivalency parameters and if the generics were bioequivalent. From the list of solid oral form of generic drugs marketed in 2012 and 2013 in the Canadian province of Quebec, we downloaded the monographs of generics from Health Canada’s website. We then proceeded to gather information on monograph availability, whether they respected Health Canada’s guidelines and if they were bioequivalent. Our study revealed that in 2012, there were 254 eligible generics, 9.8% of them had no monograph available and only 47.6% were available in both of Canada’s official languages. Similarly for 2013, there were 227 eligible generics, 7.0% of them had no monograph available and only 41.0% were available in both of Canada’s official languages. Overall, only 57.09% of generics in 2012 and 65.20% of generics in 2013 were shown bioequivalent to their reference drug. This data indicates that health care professionals amongst others, lack crucial information to make a responsible decision on the use of generics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".