Information About Canadian Patient Groups’ Conflicts of Interest and Industry Funding—Incomplete, Inconsistent, and Unreliable: A Cross-Sectional Study
Bibliographic record
Abstract
Patient groups play an important role in health care. At the same time, the majority of Canadian groups receive payments from pharmaceutical companies, which calls into question whether they speak for the best interests of their membership or the companies that fund them. Canada lacks any mandatory reporting by either patient groups or pharmaceutical companies regarding payments between groups and companies. There are three potential sources of information on the topic of payments: ( a ) declarations made by groups when they file submissions to the Canadian Agency for Drugs and Technologies in Health, an organization created and funded by Canada's federal, provincial and territorial governments, about whether the agency should recommend public funding for the new drug; ( b ) patient groups’ websites; and ( c ) voluntary disclosures by pharmaceutical companies on their websites. This study investigates the data available in all three sources and finds that they are incomplete and inconsistent, making any conclusions about patient groups’ conflicts of interest and funding unreliable. Although increased transparency is no guarantee of independence, it is an important and necessary first step. However, relying on voluntary disclosure is not sufficient. Legislation, such as the bill passed in the province of Ontario but never implemented, mandating disclosure by companies of payments that they have made is necessary.
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 | 0.020 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".