New drugs with novel therapeutic characteristics. Have they been subject to randomized controlled trials?
Bibliographic record
Abstract
OBJECTIVE: To determine how many randomized controlled trials on the safety or efficacy of new drugs are published when these drugs are first marketed in Canada, and to determine the quality of the information in those trials. DESIGN: A MEDLINE search was conducted on each drug identified as having novel therapeutic characteristics and first marketed between 1990 and 2000. MAIN OUTCOME MEASURES: Number of trials dealing with the safety or efficacy of each drug published at the time the drug was marketed. Number of patients taking the study drug, length of the trial, and type of control. RESULTS: The number of trials varied substantially. For some drugs, there were more than 20 studies; for others only a single study. Many trials were small and short-term, and used placebo controls. CONCLUSION: Too few trials or inadequate trials on the safety and efficacy of new drugs are published when these drugs are first marketed in Canada. The lack of published trials means that physicians do not know whether results are generalizable to their patients, how to position the drug in relation to other treatments, or whether the drugs have long-term safety and efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.171 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".