Clinical practice guidelines in Brazil – developing a national programme
Bibliographic record
Abstract
In Brazil, governmental and non-governmental organisations develop practice guidelines (PGs) in order to optimise patient care. Although important improvements have been made over the past years, many of these documents still lack transparency and methodological rigour. In order to conduct a critical analysis and define future steps in PG development in Brazil, we carried out a structured assessment of strengths, weaknesses, opportunities and threats (SWOT analysis) for the development of a national guideline programme. Participants consisted of academia, methodologists, medical societies and healthcare system representatives. In summary, the PG development process has improved in Brazil and current investments in methodological research and capacity-building are ongoing. Despite the centralised processes for public PGs, standardised procedures for their development are not well established and human resources are insufficient in number and capacity to develop the amount of trustworthy documents needed. Brazil's capacity could be strengthened and initial efforts have been made such as the adoption of standards proposed by world-renowned institutions in PG development and enhancement of the involvement of key stakeholders. Further steps involve the alignment between health technology assessment and PG processes for synergy and the development of a national network to promote the interaction between groups involved in the development of PGs. The lessons learned from this paper could be used to foster debate on guideline development, especially for countries facing similar threats on this topic.
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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 | 0.130 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".