Bibliographic record
Abstract
Health Canada is proposing to update its accelerated review pathways to get important new drugs into the market more quickly.To date, the two pathways that Health Canada uses have not demonstrated that they can identify therapeutically valuable new drugs.Drugs approved under the two pathways also have a greater likelihood of acquiring a serious safety warning post-marketing compared with drugs approved through the standard review pathway.The new proposals from Health Canada will not go far in rectifying this situation, and major changes are needed.Health Canada needs to present evidence that the changes it is proposing will actually allow these pathways to fulfill the set objectives and support health benefits for Canadians. RésuméSanté Canada propose une mise à jour de ses processus d' examen accéléré pour mettre plus rapidement sur le marché de nouveaux médicaments importants.À ce jour, il n' est pas démontré que les deux processus employés par Santé Canada permettent d'identifier de nouveaux médicaments qui soient profitables sur le point thérapeutique.Les médicaments
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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 | 0.083 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.071 | 0.031 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".