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Record W2804681083 · doi:10.1016/j.ekir.2018.05.007

Comparisons of Guidelines and Recommendations on Managing Antineutrophil Cytoplasmic Antibody–Associated Vasculitis

2018· review· en· W2804681083 on OpenAlexaffabout
Duvuru Geetha, Qiuyu Jin, Jennifer Scott, Zdenka Hrušková, Mohamad Hanouneh, Mark A. Little, Vladimı́r Tesař, Philip Seo, David Jayne, Christian Pagnoux

Bibliographic record

VenueKidney International Reports · 2018
Typereview
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsMount Sinai HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineVasculitisIntensive care medicineRheumatologyAnti-neutrophil cytoplasmic antibodyRandomized controlled trialMicroscopic polyangiitisRheumatismMEDLINEInternal medicineDisease

Abstract

fetched live from OpenAlex

Antineutrophil cytoplasmic antibodies-associated vasculitis (AAV) is associated with high morbidity or mortality, especially if not promptly diagnosed and treated. Many inroads have been made in the understanding of the pathophysiology that leads to exploration of novel therapies. Randomized controlled trials over the last 2 decades have better delineated and expanded therapeutic options and set the stage for an evidence-based approach. Since 2014, 4 scientific societies have systematically reviewed the existing data and have formulated evidence-based recommendations for the management of AAV. These recommendations cover diagnosis, remission induction and maintenance treatment, and prevention of long-term complications. This review is a comparative analysis of the recently published recommendations of the European League Against Rheumatism/European Renal Association-European Dialysis and Transplant Association, the British Society of Rheumatology, the Canadian Vasculitis Research Network, and the Brazilian Society of Rheumatology, and aims to determine common ground among them and highlights the differences among the recommendations.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.391
Teacher spread0.331 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
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

Citations50
Published2018
Admission routes2
Has abstractyes

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