© 2003 Canadian Medical Association or its licensors
Bibliographic record
Abstract
The marked increase in spending on drugs 1 has led payers such as provincial governments to restrict funding for many drugs to specific clinical indica-tions that are thought to be cost-effective.2,3 Some have ar-gued that this unreasonably deprives patients of access to beneficial drugs.4 In this article we argue for a new ap-proach to drug evaluation in Canada that combines the strengths of randomized trials and observational studies, and places more emphasis on the use of drug evaluation af-ter marketing for decision-making. At least 4 different types of clinical studies are required to inform rational drug policy: (1) randomized trials to de-termine efficacy and safety (which are required for licens-ing), (2) real-world randomized trials to determine effec-tiveness and safety in regular practice, (3) observational studies that use administrative databases and (4) targeted primary data collection (Table 1). Currently, most random-ized trials are done to determine efficacy and safety under ideal conditions, whereas the other designs, which are less frequently used, attempt to determine a drug’s pattern of use and effectiveness under real-world conditions. For some drugs, the results of randomized trials of efficacy will be so straightforward and the possibility of real-world use outside the conditions of the trial so small that no other study designs will be required. However, for other drugs there may be concern about the impact of the drug upon clinically important outcomes (e.g., if surrogate outcomes were used in the efficacy trials) or concern that the drug Gaps in the evaluation and monitoring of new pharmaceuticals: proposal for a different approach
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.792 | 0.638 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".