Inventors as Investigators: The Ethics of Patents in Clinical Trials
Bibliographic record
Abstract
CONTEXT: The Bayh-Dole Act and renewed emphasis on translational research have stimulated patenting activities at universities. PURPOSE: To examine how different institutions manage possible patent-related conflicts of interest in human subjects research, and to provide an ethical analysis and recommendations. METHOD: Policies of nine major professional organizations, 13 of the largest recipients of federal biomedical funding in the United States and Canada, and 17 biomedical journals were canvassed. Disagreements in policies were used as the basis for analyzing the ethics of inventorship in clinical trials. RESULTS: Policies varied along three lines. First, some policies did not define patent inventorship as a potential conflict of interest. Second, some of those that did define it as such used licensing as a trigger for conflict of interest management. Third, several policies imposed presumptive restrictions on an investigator's participation in a trial involving his or her invention. CONCLUSIONS: The author defends on ethical grounds restrictive policies on patent holding in clinical trials and rejects objections to restrictive policies. The author recommends five policies: (1) any related patent holding should always be disclosed to IRBs and research subjects, (2) investigators who hold conflicting patents should be presumptively barred from certain activities in a study, (3) institutional interests in patents should be managed and disclosed to research subjects, (4) IRBs should also be informed of an investigator's filed (not just held) patents on an experimental agent, and (5) the stringency of policies should be adjusted according to a patent's earning potential and the risk associated with a study.
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 | 0.463 | 0.608 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.087 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.031 | 0.027 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".