Are Patents Impeding Medical Care and Innovation?
Bibliographic record
Abstract
BACKGROUND TO THE DEBATE: Pharmaceutical and medical device manufacturers argue that the current patent system is crucial for stimulating research and development (R&D), leading to new products that improve medical care. The financial return on their investments that is afforded by patent protection, they claim, is an incentive toward innovation and reinvestment into further R&D. But this view has been challenged in recent years. Many commentators argue that patents are stifling biomedical research, for example by preventing researchers from accessing patented materials or methods they need for their studies. Patents have also been blamed for impeding medical care by raising prices of essential medicines, such as antiretroviral drugs, in poor countries. This debate examines whether and how patents are impeding health care and innovation.
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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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 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".