Industry funding of patient groups: a systematic review
Bibliographic record
Abstract
Abstract Background Patient groups play an important role in health care and policy. Concerns have been raised about the financial ties between the pharmaceutical industry and patient groups, because of potential threats to the groups' independence. We conducted a systematic review to synthesise studies that explored pharmaceutical or medical device industry funding of patient groups. Methods We searched Medline, Embase, Web of Science, Scopus and Google Scholar (from inception to January 2018). We included observational studies reporting at least one of the following outcomes: prevalence of industry funding; proportion of industry funded patient groups which disclosed information about this funding; association between industry funding and organisational positions on health and policy issues. We carried out duplicate independent data extraction and assessed study quality. Results 26 cross-sectional studies were included. Fifteen studies assessed the prevalence of industry funding, which ranged from 20% (12/61) to 83% (86/104). The proportion of patient groups which disclosed funding information on their websites was low (27% [95% CI: 24%-31%]). Few patient groups had formal policies governing corporate sponsorship (range from 2% (2/125) to 64% (175/274)). Among the few studies examining funding status versus organisational position, industry sponsored groups tend to hold positions consistent with sponsors' interests. Conclusions We found widespread indications of industry funding of patient groups. Transparency of funding is inadequate and the prevalence of policies governing corporate sponsorship is low. Research on policy impact is still limited. Considering the important role that patient groups play in health, strategies to prevent biases that may favour commercial interests above those of patients need to be implemented. Key messages Industry funding of patient groups is common in high income countries. Transparency of funding is inadequate and the prevalence of policies governing corporate sponsorship is low. Considering the important role that patient groups play in health care and policy, strategies to prevent biases that may favour commercial interests above those of patients need to be implemented.
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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 | 0.029 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".