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Record W3016350662 · doi:10.1016/s2665-9913(20)30095-3

Rheumatic disease and COVID-19: initial data from the COVID-19 Global Rheumatology Alliance provider registries

2020· article· en· W3016350662 on OpenAlexafffund
Milena Gianfrancesco, Kimme L Hyrich, Laure Gossec, Anja Strangfeld, Loreto Carmona, Elsa F Mateus, Paul Sufka, Rebecca Grainger, Zachary S. Wallace, Suleman Bhana, Emily Sirotich, Jean W. Liew, Jonathan S. Hausmann, Wendy Costello, Philip C. Robinson, Pedro Machado, Jinoos Yazdany

Bibliographic record

VenueThe Lancet Rheumatology · 2020
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsMcMaster UniversityImpactCanadian Arthritis Patient Alliance
FundersCilagUCB PharmaUCLH Biomedical Research CentreManchester Biomedical Research CentrePfizer UKUniversity of California, San FranciscoMedacNational Institutes of HealthMylanMcMaster UniversityUniversity of OtagoUniversity College London Hospitals NHS Foundation TrustUniversity of WashingtonUniversity College LondonAbbVieRheumatology Research FoundationNovartisCelgenePfizerBiogenNational Institute for Health and Care ResearchMassachusetts General HospitalChildhood Arthritis and Rheumatology Research AllianceMeso Scale DiagnosticsNational Institute of Arthritis and Musculoskeletal and Skin DiseasesSanofiCelltrionRocheSamsungBristol-Myers SquibbPfizer AustraliaAstraZenecaEli Lilly and Company
KeywordsCoronavirus disease 2019 (COVID-19)RheumatologyMedicine2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AlliancePandemicInternal medicineRheumatic diseaseDiseaseVirologyInfectious disease (medical specialty)OutbreakPolitical science

Abstract

fetched live from OpenAlex

Individuals with inflammatory rheumatic disease require special consideration with regard to coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Many of these individuals are considered at-risk for serious infections due to their immunocompromised state resulting from their underlying immune conditions and use of targeted immune-modulating therapies such as biologics.1–4 However, some disease-modifying drugs commonly used to treat rheumatic diseases, such as hydroxychloroquine, are being investigated as potential therapies for COVID-19.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.014
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.136
GPT teacher head0.383
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations219
Published2020
Admission routes2
Has abstractno

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