Broadening the conversation on the TRIPS agreement: Access to medicines includes addressing access to medical devices
Bibliographic record
Abstract
Patent laws determine access to medicines and medical devices, and all members of the World Trade Organization (WTO) are obligated to introduce minimum standards of intellectual‐property protection into their national patent laws. In the negotiations that led up to the Trans‐Pacific Partnership Agreement (TPP), in 2016, the United States attempted to introduce patents for diagnostic, therapeutic, and surgical methods to promote the interests of its pharmaceutical and medical‐device industries. These attempts were unsuccessful; however, these actions demonstrate the determination of those who advocate for higher standards of intellectual‐property protection to push for a TRIPS‐plus agenda. The United States has sought to limit the use of flexibilities in the TRIPS Agreement, including the use of compulsory licenses which allows the generic industry to produce cheaper pharmaceuticals. Despite these US actions, many developing countries are becoming emboldened and are issuing compulsory licenses. The position of this paper is to show that, while access to pharmaceuticals and the ability to issue compulsory licenses is crucial to administering proper health care to people living in developing countries, medical devices are equally essential. Therefore, the conversation around access to medicines should be broadened to include access to medical devices in developing countries.
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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 | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.017 | 0.036 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.050 | 0.035 |
| Insufficient payload (model declined to judge) | 0.014 | 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".