Citations in Life Science Patents to Publicly Funded Research at Academic Medical Centers
Bibliographic record
Abstract
BACKGROUND: The contributions of Academic Medical Centers (AMCs) to biomedical innovation have been difficult to measure because of the challenges involved in tracing knowledge flows from their origin to their uses. METHODS: The authors examined patent citation linkages between AMC research funded by the National Institutes of Health (NIH) and patents. In prospective analyses, they examine the extent to which articles resulting from NIH grants to AMCs awarded between 1990 and 1995 were cited in drug and medical patents. The authors then examine the extent to which these patents are associated with marketed drugs. In retrospective analyses, they examine the share of drugs approved between 2000 and 2009 that have citation links to NIH-funded AMC research. RESULTS: The prospective analyses show over a third of AMC grants resulted in publications that were cited in patents. Most the patents are drug and biotechnology patents, and are assigned to private firms. Patents citing NIH-funded AMC publications were associated with 106 new FDA approved drugs, half of which are new molecular entities and a quarter of which are priority NMEs. The retrospective analyses showed that about half of the new molecular entities approved over the 2000-2009 period had citations links to NIH-funded AMC research. CONCLUSIONS: There are strong links between articles from NIH-funded AMC research and private sector medical patenting, including drugs. More research is needed to better understand the types of links the citations represent and their implications for public policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".