Bibliographic record
Abstract
More than 80 percent of clinical drug trials in Canada are funded by the pharmaceutical industry. This article evaluates the overall state of clinical trials in Canada and looks at the interplay between public and private interests. Health Canada has adopted standards developed by the International Conference on Harmonization, a body that is heavily influenced by industry. Commercial interests are increasingly involved in recruiting patients into clinical trials and in running these trials. It is in industry's interests to conduct drug tests on people for which it is easiest to see benefits. These interests are not fundamentally challenged by Health Canada's policy of issuing nonmandatory guidelines on who should and should not be included in clinical trials. The outcome of clinical trials is heavily influenced by commercial sponsorship, with the result that trials may favor corporate interests rather than the interests of the public. How Health Canada deals with that possibility is not known, because of its strict policy of treating clinical trial data as private property. If clinical trials are to serve the purpose for which they are designed, developing reliable and objective information about new drugs, then commercial interests cannot be allowed to take precedence over health interests.
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 | 0.086 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.035 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 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".