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TYPE 2 LUPUS IS THE MAJOR PREDICTOR OF FATIGUE IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS: A CROSS-SECTIONAL STUDY OF 200 PATIENTS

2025· article· en· W4410513180 on OpenAlexvenueno aff
Sofia Ferreira Azevedo, Marcelo Neto, Cláudia P. Oliveira, Anna Mattiuzzo, José António Pereira da Silva, Luís Inês

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCross-sectional studySystemic lupus erythematosusSystemic diseaseLupus erythematosusInternal medicineSystemic lupusDermatologyImmunologyImmunopathologyPathologyDiseaseAntibody

Abstract

fetched live from OpenAlex

PV175 / #503 Poster Topic: AS19 - Patient-Reported Outcome Measures Background/Purpose Fatigue is a major symptom in patients with systemic lupus erythematosus (SLE) significantly impacting health-related quality of life (HR-QoL). Causes of fatigue in SLE are multifactorial and not well understood. A novel framework categorizes SLE manifestations into type 1 and type 2. Type 1 manifestations can be ascribed to inflammation and captured in activity indexes, whereas type 2 symptoms (such as fatigue, pain, sleep, and mood disturbances) have no clear relation to disease activity. Type-2 SLE is defined using the polysymptomatic distress scale (PDSS). Objective: To identify predictors of fatigue in SLE patients. Methods Cross-sectional study of SLE patients fulfilling ACR/EULAR 2019 and/or SLICC 2012 classification criteria followed in a Rheumatology outpatient clinic. Inclusion was from December 2023 to May 2024. Results The study included 200 patients with SLE (female: 92.0%; mean age: 46.4±14.3 years; median age at diagnosis: 28.5 [16.0] years). Severe fatigue was present in 28.6%. In the study population, 87.5% fulfilled the LDA treatment target, 30.5% had type 2 SLE, 38.5% had organ damage, and 17.0% were diagnosed with fibromyalgia. Patients with severe fatigue had worse scores in both PDSS and aPDSS (p<0.0001). The aPDSS cut-off that better predicted SLE type 2 was ≥9.50 (sensitivity 96.9%, specificity 99.9%). Univariate analysis showed significant variables for severe fatigue: female sex (p=0.04), age (p=0.02), disease duration (p=0.02), SLE-DAS (p=0.03), type 2 SLE (PDSS and aPDSS definitions) (p<0.001), and fibromyalgia (p<0.001), while LDA was protective (p<0.01). SLEDAI and SDI were not significant. Multivariate analysis revealed type 2 SLE [OR 25.33; 95% CI(10.63-60.31); p<0.001) and diagnosis of fibromyalgia [OR 3.51; 95% CI(1.16-10.69); p<0.05] as independent predictors of severe fatigue. Similar findings were found when using aPDSS, [OR 16.16; 95% CI(7.22-36.13); p<0.001)] for type 2 SLE and [OR 4.44; 95% CI(1.51-12.26); p<0.05] for fibromyalgia. Conclusions In this cohort of SLE patients, type 2 lupus was the major predictor of severe fatigue. Physicians and patients should be careful not to attribute fatigue to inflammatory type 1 disease activity when the LDA treatment target is achieved. This should be taken into consideration for establishing the management strategy. * First and second authors listed share first authorship.

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)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.299
Teacher spread0.282 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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