Could conditional release of new drugs provide the information required to study drug effectiveness? - A discussion paper.
Bibliographic record
Abstract
Conditional release is the approval of a new drug onto the market, subject to specific conditions relating to effectiveness and safety that, if achieved, will lead to full approval. Conditional approval of a new drug, during which time it is used in normal clinical practice, should allow the collection of data on effectiveness and safety to provide a genuine cost effectiveness evaluation. Proposals put forward in 1977 in the United Kingdom for approving a drug on a conditional basis while monitoring for adverse drug reactions are examined, and issues that would affect a present day conditional release scheme are identified. These issues are: who would do the evaluation and who would pay for it; how would patients be identified and registered; would all new drugs be monitored and for how long; what data would be reported and evaluated; and who would do the reporting? How a conditional release scheme would work in Canada in light of these questions is considered and a method based on pharmacists registering patients and on physicians and/or patients reporting data to an independent organization funded by governments and the pharmaceutical industry is outlined. Under certain conditions, conditional release would provide the information to allow true cost effectiveness and safety assessments instead of the current inadequate predictions based on efficacy and safety data from clinical trials. It is important that academics and drug approval and monitoring agencies work together to develop active systems to improve the postapproval evaluation of effectiveness, safety and cost effectiveness of new drugs in Canada.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".