© 2002 Canadian Medical Association or its licensors Letters
Bibliographic record
Abstract
Inaccessibility of drug reports When new drugs are launched,physicians must have access to the randomized controlled trials that evaluated their efficacy and safety. I wrote to 12 Canadian pharmaceuti-cal companies, all subsidiaries of multi-national companies, who released a total of 16 new drugs from 1990 to 1999. I asked them to supply me with a list of the randomized controlled trials on the primary indication for each product that were published in English and that were available to physicians at the time the product was first marketed in Canada. A second letter was sent to all companies that did not respond after 5 weeks. Two of the 12 companies did not re-spond and one said it was unable to compile the necessary data. Of the oth-ers, only GlaxoSmithKline accurately complied with my request, sending ma-terial on one study for one of its prod-ucts (it was asked to provide informa-tion on 3 products in total). Other companies sent extraneous material, in-cluding studies that had been published in other languages, studies published after the product had been marketed and studies evaluating uses of the prod-uct other than that for which it was pri-marily marketed. Interested readers can contact me for a complete list of these studies and drugs. This variability in the responsiveness of pharmaceutical companies is not a new phenomenon.1 All of the companies in question are members of Canada’s Research-Based
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.014 | 0.006 |
| Insufficient payload (model declined to judge) | 0.605 | 0.412 |
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".