Can a good tree bring forth evil fruit? The funding of medical research by industry
Bibliographic record
Abstract
BACKGROUND: Systematic reviews analysing the influence of funding on the conduct of research have shown how Conflicts of Interest (COIs) create bias in the production and dissemination of data. SOURCES OF DATA: The following is a critical analysis of current opinions in respect to COIs created by industry funding of medical research in academic institutions. AREAS OF AGREEMENT: Effective mechanisms are necessary to manage COIs in medical research, and to prohibit COIs that clearly affect validity of research conduct and outcomes. AREAS OF CONTROVERSY: While most hold that industry investment in university research is not a barrier to good science, there are questions about how securing funding opportunities might be prioritized over the risks of potential COIs. It is argued that COIs are inherent risks to research integrity, requiring the strengthening of current governance frameworks. GROWING POINTS: The focus on COIs, created by the ostensibly categorical actions of industry, challenges the evolving research priorities within academic institutions. AREAS TIMELY FOR DEVELOPING RESEARCH: Less well-defined COIs are equally culpable to financial ones, in terms of the systemic damage they do to science. So, are they appropriately managed as risks within university research settings?
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 | 0.049 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".