O processo de incorporação de tecnologias em saúde no Brasil em uma perspectiva internacional
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given the financial impact of the adoption of new health technologies in health systems, choosing what technology should be introduced and when poses a major challenge for health managers. The health technology assessment (HTA) process should therefore be underpinned by transparent and objective criteria. The objective of this study was to analyze HTA processes in Brazil, overseen by the National Commission for the Incorporation of Health Technology (CONITEC), and to compare these processes with those in countries considered to be at the forefront of this field: Australia, Canada, and the United Kingdom. The following categories were used for the comparative analysis: program structure, definition and selection of topics, evidence review, use of HTA in decision making, program products and dissemination, and transparency. The findings show that there are more similarities than differences between these countries' processes and the CONITEC processes. The main differences identified were: composition of committees, entitlement to appeal, program evaluation, and timeframes for the implementation of recommendations/decisions. Despite making major strides in recent years, Brazil should continue to promote continuous improvement of its HTA process.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.032 |
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 it