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Effect of vitamin D treatment in ANCA-associated vasculitis: results from an exploratory perspective, pragmatic, non-randomised study

2025· article· en· W7135184869 on OpenAlexaffabout
Ava Basti, Lina Zgaga, Irena Doubelt, Medha Soowamber, Christian Pagnoux

Bibliographic record

VenueClinical and Experimental Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsVitamin D and neurologyMEDLINERheumatologyvitamin D deficiency

Abstract

fetched live from OpenAlex

OBJECTIVES: Vitamin D deficiency has been linked with several autoimmune diseases. Data are limited in anti-neutrophil cytoplasm autoantibody (ANCA)-associated vasculitis (AAV), and it is unknown whether vitamin D could have a therapeutic role in AAV. METHODS: The prospective, pragmatic, non-randomised exploratory PRAVDA study with ITT and pre-protocol analyses aimed to enrol >100 patients with AAV at the Vasculitis Clinic (Toronto, Canada) from January to July 2021. 25-hydroxyvitamin D [25(OH)D] was measured at baseline by ELISA. Patients with low 25(OH)D (<75 nmol/L at baseline) were asked to increase vitamin D supplementation by 1000 IU/day (to a maximum 2000 IU/day). 25(OH)D was measured again at month 12. The primary endpoint was 12-month disease relapse. Secondary analyses included correlations between vitamin D status and disease-specific clinical features. RESULTS: The study included 101 patients, 41 (40.6%) of whom had low baseline vitamin D levels and were asked to increase vitamin D3 intake. Of these patients, 32 had vitamin D level reassessed at month 12; 62.5% (20/32) had achieved normal levels. Relapse rates at month 12 were similar between patients with low (n=3/41; 7.3%) and normal (n=6/60; 10%; p=0.64) baseline vitamin D levels. However, no relapses were observed in patients who corrected baseline vitamin D deficiency. CONCLUSIONS: These findings can help designing larger studies on vitamin D supplementation in AAV patients, focusing mostly on those vitamin D deficient at baseline.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.365
Teacher spread0.351 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes2
Has abstractyes

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