PP495 Addressing The Interactions Between Health Regulation And Health Technology Assessment In Brazil
Bibliographic record
Abstract
Introduction The interaction of health technology assessment (HTA) and health regulatory agencies has been widespread, especially for decision-making in health system coverage. The objective of this paper is to report the HTA-regulatory interaction in Brazil. Methods This is a case study on the interaction between HTA and regulation in Brazil. Technical documents and Brazilian legislation on health regulation and HTA were analyzed. The study was conducted in July 2019. Results HTA-Regulatory Interaction in Brazil is still incipient. There is no responsible agency for interaction between agencies, as there is in Europe and Canada, for example. In the last 4 years, cooperation has started between the Brazilian Health Surveillance Agency (Anvisa) and the Oswaldo Cruz Foundation (Fiocruz) for post-registration monitoring of medicines. During this partnership, 170 post-marketing drug opinions were prepared, assisting the regulatory agency in decision-making. Conclusions Brazil legislation guarantees essential medicines at low cost or free. The interaction between HTA and regulation has the potential to reduce the time taken to incorporate technology to the patient, in addition to ensuring greater safety for users of the Unified Health System. In this sense, it was observed that the interaction between health regulation and science and technology institutions has innovative potential in this approach.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".