Chapter 7. Follow-Up of Drugs After Market Entry
Bibliographic record
Abstract
There is a general agreement with the fact that our operational knowledge on drugs at the time they enter the market is grossly inadequate. It is also generally accepted that, in most cases, delaying entry into the market by requesting additional animal or clinical studies would not answer the remaining questions and would only delay the patients ’ access to useful and sometimes life-saving drugs (1). The solution is therefore to continue studying drugs in a formal way for an indeterminate period of time following their entry into the market (2). Those who consider indeterminate too long a period of time might wish to recall the cisapride experience. Although few active participants and observers of the medication scene would disagree with the above statements, there is considerable confusion and indecision as to how to proceed in a practical and economical way in order to answer the numerous questions that still remain at the time of entry into the market. It is important to realise at this point that the question is not necessarily global or universal, and that it has important connotations in regard to specific countries. This is due to the fact that some countries are traditionally allowing drugs into the market sooner than others (3). This analysis will therefore be done from a Canadian perspective, which takes into account the fact that most drugs have been marketed in the United States and/or the European Union from 6 to 12 months before being allowed access to the
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".