ANCA-Associated Vasculitis in Latin America
Bibliographic record
Abstract
ABSTRACT: Most of the existing literature, including epidemiological studies and clinical trials, on antineutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) include North American (mainly United States and Canada), European, and Asian populations. Few studies have focused on multiethnic populations such as the one from Latin America. Racial and ethnic differences in the incidence of AAV could partially explain the comparatively low number of AAV studies originating in Latin America. However, given the racial/ethnic diversity as well as socioeconomic differences existing in this region, better reporting of AAV presentations and outcomes in Latin America could highlight valuable gaps on the understanding and treatment of these patients. Recently, larger case series and studies have provided better clinical information regarding AAV patients in Latin American countries; however, further information is needed to address gaps such as risk factors, genetic profiles, clinical features, and predictors of clinical outcomes. For these reasons, we have performed a systematic literature review to enhance our understanding of AAV patients in Latin America. We have included 11 articles focused on the epidemiological and clinical features of AAV in Latin America; some similarities and differences with AAV in other regions are shown in these articles. We have identified differences in their prevalence across Latin American countries, which may reflect reporting bias or true ethnic differences among the countries. Our findings should encourage further investigation into AAV in Latin America; such studies will hopefully lead to the optimal management of these patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".