Why “American Patients First” is likely to raise drug prices outside of the United States
Bibliographic record
Abstract
Background: The Trump administration’s ‘American Patients First’ blueprint proposes to reduce drug prices in the USA by increasing drug prices abroad, ex USA. The possibility of the Trump administration to raise drug prices ex USA through legal action via the WTO and bilateral negotiations with foreign trade partners was reviewed.Methods: A literature review was conducted through PUBMED, EMBASE, Media and grey literature to consolidate publications of the Trump administrations’ policies and strategies towards foreign countries and drug prices.Results: The Trump administration has withdrawn from and halted major multilateral agreements including the TPP, Paris Agreement, TTIP, UNESCO, NAFTA (now USMCA), and NATO. The Trump administration has been successful in bilateral negotiations for pharmaceuticals’ pricing, as seen with Japan, South Korea, Germany, and Mexico and Canada.Conclusion: The objective of raising prices abroad is attainable. Action through the WTO is unlikely, due to its nondiscriminatory principle. Bilateral trade negotiation have proven more promising. In this bilateral framework, financial security and military protection are strong assets for the USA to levy higher drug prices abroad. Although raising drug prices ex USA is possible, further questions as to whether this will directly translate into lower drug prices for American patients are raised.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".