Incorporação de tecnologias nos sistemas de saúde do Canadá e do Brasil: perspectivas para avanços nos processos de avaliação
Bibliographic record
Abstract
One of the main challenges for modern health systems is to guarantee equitable access to technologies with proven quality, safety, efficacy, and cost-effectiveness, as well as to ensure that their use is based on high-quality scientific evidence. Health technology assessment (HTA) is one of the most widely used strategies in the world to support decisions on health technologies. The article analyzes how HTA systems are organized in Brazil and Canada and discusses the implications for planning the incorporation of technologies in Brazil, considering the challenges posed by the regionalization process and the establishment of healthcare networks. This is an exploratory comparative study based on secondary data. The results show that both countries have fragmented HTA systems with different levels of maturity. The systems are characterized by multiple organizations working in the field of HTA, the scope of activities, and the concentration of activities in national agencies/bodies. Both systems have weaknesses, but the Brazilian case presents a series of factors (insufficient resources, impact of court rulings, heavy dependence on foreign technologies, and incipient regional HTA processes and planning) that make the scenario more complex. The article argues that the regionalized structure for planning the incorporation of technologies in Canada can serve as an interesting experience for the Brazilian system, despite the different contexts in the two countries.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".