Is Thailand's hepatitis C treatment program at risk from its plans to join an Asia-Pacific trade pact?
Bibliographic record
Abstract
Thailand has expressed interest in joining the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), an 11-country plurilateral trade agreement whose original incarnation included the United States of America (USA). When the USA withdrew from this agreement, key intellectual property clauses relevant to pharmaceuticals were suspended. These could be reinstated should the USA decide to re-join. This study aimed to measure the impact of these suspended clauses by costing Thailand’s 2020 hepatitis C treatment program under four scenarios: 1) existing treatment regime, which does not use the currently recommended treatment regime and does not source the lowest price medicines, 2) treatment regime if Thailand joined the CPTPP and suspended clauses were reinstated, 3) treatment regime if Thailand utilised flexibilities in international law enabling access to the cheapest direct acting antivirals on the global market and 4) lowest-cost generic pan-genotypic regime on the global market. Joining the CPTPP would increase the cost of Thailand’s hepatitis C treatment program more than tenfold if suspended CPTPP clauses were reinstated and TRIPS flexibilities not fully utilised. Within the existing budget, the price and regime for scenario 4 would enable an additional 7,571 people to access hepatitis C treatment and avoid the need for genotype testing. Signing trade agreements such as the CPTPP that require stronger intellectual property protections could compromise Thailand’s hepatitis C program and other national treatment programs reliant on affordable generic medicines and prevent it from relying on its own pharmaceutical capabilities to manufacture medicines needed to sustain its treatment programs.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".