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Record W4200554823 · doi:10.1093/rheumatology/keab930

Clinical features and outcomes of COVID-19 in patients with IgG4-related disease: a European multi-centre study

2021· article· en· W4200554823 on OpenAlexaff
Giuseppe A. Ramirez, Marco Lanzillotta, Mikaël Ebbo, Andreu Fernández‐Codina, Gaia Mancuso, Olimpia Orozco-Gálvez, Lorenzo Dagna, N. Schleinitz, Fernando Martínez‐Valle, Emma Culver, Emanuel Della‐Torre

Bibliographic record

VenueLara D. Veeken · 2021
Typearticle
Languageen
FieldMedicine
TopicIgG4-Related and Inflammatory Diseases
Canadian institutionsWindsor Regional HospitalWestern University
FundersAcademy of Medical Sciences
KeywordsMedicineRituximabEpidemiologyRetrospective cohort studyConcomitantInternal medicineDiseaseCoronavirus disease 2019 (COVID-19)Observational studyPediatricsIntensive care medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Dear Editor, Patients affected by inflammatory rheumatic disorders are at increased risk of Coronavirus Disease-19 (COVID-19)-related adverse outcomes due to concomitant immunosuppressive medication and comorbidities [1, 2]. IgG4-related disease (IgG4-RD) is an increasingly recognized systemic fibro-inflammatory condition that predominantly affects elderly males whose standard of care is based on glucocorticoids and rituximab regimens, all established risk factors for poorer COVID-19 outcomes [3–7]. In addition, elevation of serum IgG4 has been recently identified as a predictor of mortality in hospitalized COVID-19 patients, raising the possibility that an immunological background prone to preferential IgG4 production may favour life-threatening SARS-CoV-2 infection [8]. In the present observational retrospective study, we collected epidemiological and clinical features of patients with biopsy proven IgG4-RD and followed at tertiary care centres in France, Italy, Spain and the UK. Patients were interrogated by phone call between December 2020 and February 2021...

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.307
Teacher spread0.290 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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