Regenerative Medicine in Brazil: Small but Innovative
Bibliographic record
Abstract
AIMS: Although Brazil has received attention for conducting one of the world's largest stem cell clinical trials for heart disease, little has been published regarding Brazil's regenerative medicine (RM) sector. Here we present a comprehensive case study of RM in Brazil, including analysis of the current activity, the main motivations for engaging in RM and the remaining challenges to development in this field. METHODS: Our case study is primarily based on semi-structured interviews with experts on RM in Brazil, including researchers, policymakers, clinicians, representatives of firms and regulators. RESULTS: Driven by domestic health needs and strategic government support, Brazil is producing innovative RM research, particularly for clinical research in cardiology, orthopedics, diabetes and neurology. We describe the main RM research currently taking place in Brazil, as well as some of the economic, regulatory and policy events that have created a favorable environment for RM development. Brazilian RM researchers need to overcome several formidable challenges to research: research funding is inconsistent, importation of materials is costly and slow, and weak linkages between universities, hospitals and industry impede translational research. CONCLUSIONS: Although Brazil's contribution to the RM sector is small, its niche emphasis on clinical applications may become of global importance, particularly if Brazil manages to address the challenges currently impinging on RM innovation.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".