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Record W2079546434 · doi:10.1191/0961203304lu2020oa

Dermatology position paper on the revision of the 1982 ACR criteria for systemic lupus erythematosus

2004· article· en· W2079546434 on OpenAlexaff
Joerg Albrecht, Jesse A. Berlin, Irwin M. Braverman, Jeffrey P. Callen, Melissa Costner, Jan Dutz, David Fivenson, Andrew G. Franks, Joseph L. Jorizzo, L A Lee, D P McCauliffe, Richard D. Sontheimer, Victoria P. Werth

Bibliographic record

VenueLupus · 2004
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMedical diagnosisDermatologyConfidence intervalDifferential diagnosisSystemic lupus erythematosusLupus erythematosusDiseaseInternal medicinePathologyImmunologyAntibody

Abstract

fetched live from OpenAlex

The 1982 ACR classification criteria have become de facto diagnostic criteria for systemic lupus erythematosus (SLE), but a review of the criteria is necessary to include recent diagnostic tests. The criteria were not developed with the help of dermatologists, and assign too much weight to the skin as one expression of a multiorgan disease. Consequently, patients with skin diseases are classified as SLE based mostly on skin symptoms. We discuss specific problems with each dermatologic criterion, but changes must await a new study. We suggest the following guidelines for such a study, aimed at revision of the criteria. 1) The SLE patient group should be recruited in part by dermatologists. 2) The study should evaluate an appropriate international ethnic/racial mix, including late onset SLE as well as pediatric patients. 3) All patients should have current laboratory and clinical evaluations, as suggested in the paper, to assure the criteria can be up-to-date. This includes anti-SS-A and anti-SS-B antibodies and skin biopsies for suspected cutaneous lupus erythematosus except for nonscarring alopecia and oral ulcers. 4) The study should be based on a series of transparent power calculations. 5) The control groups should represent relevant differential diagnoses in numbers large enough to assess diagnostic problems that might be specific to these differential diagnoses. In order to demonstrate specificity of the criteria with a 95% confidence interval between 90 and 100%, each control group of the above should have at least 73 patients.

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)0.004
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0200.017

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 head0.027
GPT teacher head0.313
Teacher spread0.286 · 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
GenreCommentary

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

Citations115
Published2004
Admission routes1
Has abstractyes

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