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Record W4393436566 · doi:10.1177/27551938241242602

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?

2024· article· en· W4393436566 on OpenAlexaff
Brigitte Tenni, Joel Lexchin, Sovath Phin, Deborah Gleeson

Bibliographic record

VenueInternational Journal of Social Determinants of Health and Health Services · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsYork University
FundersLa Trobe University
KeywordsGraduation (instrument)TRIPS architectureIntellectual propertyMedicineDeveloping countryAccessionEconomic growthEnvironmental healthBusinessInternational tradePolitical scienceEconomicsLawEngineering

Abstract

fetched live from OpenAlex

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%).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.413
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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