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Record W3020403921 · doi:10.3899/jrheum.200507

Incidence and Clinical Course of COVID-19 in Patients with Connective Tissue Diseases: A Descriptive Observational Analysis

2020· letter· en· W3020403921 on OpenAlexvenueno aff
Ennio Giulio Favalli, Elena Agape, Roberto Caporali

Bibliographic record

VenueThe Journal of Rheumatology · 2020
Typeletter
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatologyIncidence (geometry)PandemicOutbreakInternal medicineObservational studyCoronavirus disease 2019 (COVID-19)PopulationEpidemiologyConnective tissueDiseaseIntensive care medicineInfectious disease (medical specialty)PathologyEnvironmental health

Abstract

fetched live from OpenAlex

To the Editor: The outbreak of COVID-19 in December 2019 in China has very quickly become a global health emergency, with almost 2 million infected patients worldwide1. Along with the spread of the pandemic, there has been growing concern about the management of fragile patients with rheumatic conditions. There are still very few data available on this aspect. In particular, subjects affected by connective tissue diseases (CTD) are known to have an increased infectious risk compared to the healthy population because of a general impairment of the immune system intrinsic to the autoimmune disease itself, the iatrogenic effect linked to the use of immunosuppressive drugs, and the high number of comorbidities that often complicate the clinical picture2,3. On the other hand, the progressive increase in the knowledge about the pathogenesis of the infection is paving the way for the use of certain drugs common in rheumatology to also treat COVID-194. As rheumatologists operating in one of the major epicenters … Address correspondence to Dr. E.G. Favalli, Division of Clinical Rheumatology, ASST Gaetano Pini-CTO Institute, Via Gaetano Pini 9, 20122 Milan, Italy. E-mail address: enniofavalli{at}me.com

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)0.007
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 score0.128
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.030
GPT teacher head0.342
Teacher spread0.312 · 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

Citations95
Published2020
Admission routes1
Has abstractyes

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