Cambodia's Imminent Graduation from Least Developed Country Status: What Will be the Impact of the TRIPS Agreement on Access to HIV and Hepatitis C Medicines in Cambodia?
Bibliographic record
Abstract
Cambodia has experienced exponential economic growth in recent years and is expected to graduate from least developed country (LDC) status within the next decade. Membership of the World Trade Organization (WTO) will require Cambodia to grant product and process patents for pharmaceuticals upon LDC graduation. This study aims to measure the impact of the WTO Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) on the price of HIV and hepatitis C medicine in Cambodia once it graduates from LDC status and is obliged to make patents available for pharmaceutical products and processes. Using scenarios based on likely outcomes of accession to the TRIPS Agreement, it measures the impact on the price of the HIV treatment program and compares that impact with the hepatitis C treatment program. Graduation from LDC status would be expected to result in a modest increase in the cost of the antiretroviral (ARV) treatment program and very large increases in the cost of the direct acting antivirals (DAA) treatment program. If annual treatment budgets remain constant, patent protection could see 1,515 fewer people living with HIV able to access ARV treatment and 2,577 fewer people able to access DAA treatment (a drop in treatment coverage of 93%).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".