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Record W4200140646 · doi:10.1097/rhu.0000000000001827

ANCA-Associated Vasculitis in Latin America

2021· review· en· W4200140646 on OpenAlexaboutno aff
Víctor R. Pimentel-Quiroz, Sebastian E. Sattui, Manuel F. Ugarte‐Gil, Graciela S. Alarcón

Bibliographic record

VenueJCR Journal of Clinical Rheumatology · 2021
Typereview
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansANCA-Associated VasculitisVasculitisMedicineDermatologyPolitical scienceInternal medicineLaw

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.101
GPT teacher head0.448
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2021
Admission routes1
Has abstractyes

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