Prediction of therapeutic value of new drugs approved by health Canada from 2011−2020: A cross-sectional study
Bibliographic record
Abstract
Objectives: To examine whether a combination of three characteristics of new drugs - review type, outcome of premarket trials (surrogate or clinical) and first-in-class is associated with significant therapeutic value. Design: Cross-sectional analysis of new drugs approved by Health Canada from January 1, 2011 to December 31, 2020. Setting: Canada. Participants: New drugs approved by Health Canada for which therapeutic evaluations, trial outcomes and first-in-class status was available. Main outcome measures: Distribution of therapeutic value (major, moderate, little to no) depending on how many of the three characteristics were present for each drug. Results: Health Canada approved 340 drugs of which 243 had data available for analysis. If all three characteristics were present 10 out of the 20 drugs had a major therapeutic rating. Conversely if none were present only 2 drugs out of 37 had a major therapeutic rating. Conclusion: This study introduces a new evaluation method for determining whether new drugs will have major therapeutic value that appears to be more successful than relying only on the type of review that drugs receive.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| 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".