Process of Incorporation and Guidance in Public Health Systems for Protease Inhibitors in Chronic Hepatitis C in: a comparative case study between Brazil, Australia, Canada and England.
Bibliographic record
Abstract
Health Technology Assessment (HTA) studies subsidize decisions regarding the rational use of health technologies. Finding a balance between the sustainability of health systems, and proper access and use of technology, is a challenge and requires the use of consistent tools. In this paper we analyzed the general characteristics of the agencies or institutions responsible for HTA in Brazil, Australia, Canada and England and we carried out a comparative case study to understand the incorporation of protease inhibitors telaprevir and boceprevir for the treatment of chronic hepatitis C in these countries. For that purpose, we used electronic published reports found on the websites of those HTA agencies regarding telaprevir and boceprevir in 2013. Compared to other agencies, the Brazilian Commission (CONITEC) unlike the others, accepted the submission and published a single report for both drugs; the HTA model was homogeneous across countries. There were differences in the eligibility criteria and target population, despite the fact of drug approval; the price charged in Brazil was the lowest; the Brazilian CONITEC also set the criteria for healthcare using the drugs and for clinical protocols and therapeutic guidelines. Keywords: BiomedicalTechnology; Hepatitis C; ProteaseInhibitors
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".