Regulatory Approval Process for Drugs in Canada-A Challenging Task
Bibliographic record
Abstract
The drugs and medical devices for human use in Canada are regulated by Health Canada's Therapeutic Products Directorate (TPD). Health Canada is responsible to implement the rules and regulations for the marketing of drugs. Health Canada's process for approving new drugs is very slow and they give approval for drug products based on a complete review of safety and efficacy data. In Canada, around 70% of the new drugs were submitted over three months, and 40% more than one year, after their first submission. For drugs that were eventually approved to be marketed in Canada and in at least one of the other jurisdictions, the average delay from the first submission in either foreign jurisdiction to submission in Canada was 540 days. A drug approval process is completed by various applications submitted to the authority and the registration of drugs in Canada is really challenging. The purpose of this article is to provide information about the procedure from pre-submission to the marketing of a pharmaceutical drug for obtaining the drug approval in Canada.
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 | 0.048 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".