Therapeutic value of oncology products with a conditional approval from Health Canada: a cross-sectional study
Bibliographic record
Abstract
Objectives: Investigate the additional therapeutic value compared to existing medicines of new oncology drugs given a conditional approval (Notice of Compliance with conditions, NOC/c) by Health Canada using therapeutic ratings from four independent organisations. Design: A list of all new oncology drugs with an NOC/c from the start of the programme in 1998 to the end of 2023 was constructed. First-in-class and orphan drug status was determined for all drugs. Therapeutic ratings were obtained from the Canadian Patented Medicine Prices Review Board, the French drug bulletin Prescrire International, the French agency Haute Autorité de Santé and the German Institute for Quality and Efficiency in Health Care. If more than one organisation rated the drug, the highest rating was used. Setting: Canada. Participants: Oncology drugs with a conditional approval. Main outcome measures: Additional therapeutic gain compared to existing products. Results: Fifty-four oncology drugs were approved. Conditions were fulfilled for 29, fulfilment was still pending for 22 and 3 drugs were either discontinued by the manufacturer or placed on restricted access. Eighteen drugs had both orphan drug and first-in-class status. Therapeutic evaluations were available for 50 drugs, and the distribution of additional therapeutic value was examined for the entire group of 50 drugs, for 29 drugs that had fulfilled their conditions and for 18 drugs with both orphan drug and first-in-class status. In the three groups, 8.0%, 10.3% and 11.7%, respectively, offered major therapeutic improvement. Conclusions: Few new oncology drugs approved through the NOC/c pathway offer major therapeutic improvements over existing drugs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".