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Record W4296031303 · doi:10.3899/jrheum.220089

Cutaneous Involvement in Systemic Lupus Erythematosus: A Review for the Rheumatologist

2022· review· en· W4296031303 on OpenAlexvenueno aff
Courtney Stull, Grant Sprow, Victoria P. Werth

Bibliographic record

VenueThe Journal of Rheumatology · 2022
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthOffice of Research and DevelopmentU.S. Department of Veterans Affairs
KeywordsMedicineConnective tissue diseaseSystemic diseaseDermatologyDiseaseLupus erythematosusCutaneous Lupus ErythematosusSystemic lupus erythematosusSystemic lupusImmunologyAutoimmune diseasePathologyAntibody

Abstract

fetched live from OpenAlex

The majority of patients with systemic lupus erythematosus (SLE) have cutaneous manifestations at some point in their disease course. The skin findings in SLE are classified as SLE-specific or SLE-nonspecific based on histopathologic findings. SLE-specific skin diseases include chronic cutaneous lupus erythematosus (CLE), subacute CLE, and acute CLE. There are subsets of skin lesions within each group and the likelihood of associated SLE varies among them. SLE-nonspecific lesions are more common in patients with SLE and tend to coincide with active systemic disease. SLE-nonspecific lesions may be seen as a feature of another disease process, including other connective tissue diseases. It is important for the rheumatologist to be familiar with the spectrum of cutaneous diseases in SLE to help prognosticate the likelihood of systemic disease and to ensure patients receive timely dermatologic care with the goal of controlling disease activity to prevent damage.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.079
GPT teacher head0.364
Teacher spread0.285 · 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 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

Citations99
Published2022
Admission routes1
Has abstractyes

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