Brazilian Research in Intensive Care Network (BRICNet): shaping the landscape of critical care research in Brazil and beyond
Bibliographic record
Abstract
Critical illnesses such as sepsis and acute respiratory distress syndrome lead to millions of deaths globally, with a higher burden in low- and middle-income countries. Conducting multicentric clinical studies is essential to help minimize the burden of critical illnesses, particularly in areas where their impact is greater. However, conducting large-scale multicentric studies is challenging, and most large multicentric studies in critical care are from high-income countries, which limits their relevance in other contexts. This highlights the need for collaborative research networks in low- and middle-income countries to better address local needs. The Brazilian Research in Intensive Care Network (BRICNet) was created by a group of intensivists and researchers in 2007 and is dedicated to being the leading organization in Brazil for conducting collaborative clinical research to improve care for critically ill patients. BRICNet focuses on investigator-initiated and collaborative studies relevant to global intensive care, with a special emphasis on Brazilian context. Its mission includes advancing research methodology, scientific writing, and conducting large-scale multicenter studies to fill knowledge gaps in critical care. Since its creation, the network has published 71 articles, including 15 randomized controlled trials and 14 observational studies, many of them in collaboration with major Brazilian institutions and international networks. This review aims to critically assess the achievements of BRICNet, highlighting its high-impact publications, international partnerships, and capacity building, which have significantly contributed to the field of intensive care. Looking ahead, we also identify barriers and solutions for sustainable growth.
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How this classification was reachedexpand
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".