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CLINICAL MANIFESTATIONS AT DIFFERENT FOLLOW-UP TIME POINTS IN AUTOANTIBODY-DEFINED SLE SUBGROUPS.

2025· article· en· W4410513130 on OpenAlexvenueno aff
Lina-Marcela Díaz-Gallo, Antonio González, Øyvind Molberg, Karoline Lerang

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Bullous Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAutoantibodyImmunopathologyInternal medicineImmunologyAntibody

Abstract

fetched live from OpenAlex

PV179 / #524 Poster Topic: AS20 - Precision Medicine Background/Purpose The heterogeneity of SLE has impaired the advancement in diagnostic strategies, tailored treatments, and prognostic tools for this disease. Defining SLE subgroups based on autoantibody profiles can reduce such heterogeneity and reveal important differences.[1] Here, we studied a Norwegian inception cohort, with longitudinal follow-up at 2 years and 11 years (in median IQR[7-13]) after diagnosis. Methods We clustered 102 SLE patients, as previously,[1] based on 10 autoantibodies at diagnosis (Table 1, Figure 1A). The autoantibodies were measured using ELISA or immunoprecipitation, we assumed positivity if 1 of them was positive. Using logistic or linear regression, we tested associations between clusters and 13 clinical manifestations at diagnosis, 2-year, and last visit. We tested associations between the clusters and SLEDAI and some of its components: acute cutaneous lupus, cardiovascular disease, lung, or muscle-skeletal involvement for the last follow-up. Analyses were done in R v4.3.3. Table 1. Characteristics of the SLE cohort from Norway. Figure 1. Results Four clusters explained most variability based on the Silhouette index (Figure 1). The patients in subgroup 2 have a higher risk of photosensitivity at diagnosis (OR:12.5 95% CI:2.1-252.8) and 2 years after (OR:8.1 95% CI:1.7-76.8). In comparison, photosensitivity was less common in subgroup 3 compared to the rest of patients at 2 years (OR:0.3 95% CI:0.09-0.7) (Figure 2B). Acute cutaneous lupus was more frequent in subgroup 2 at the last visit (OR:3.7 95% CI:1.2-14.1), lung involvement was more frequent in subgroup 1 (OR:14.5 95% CI:11.4-543.2), although it did not reach significance (p-value=0.053). SLEDAI significantly differed among the subgroups at diagnosis being higher for subgroups 4 and 3 (respectively: OR:1.5 95% CI:1.3-1.8; OR:1.3 95% CI:1.1-1.5), conversely lower for subgroups 2 and 1 (respectively: OR:0.7 95% CI:0.6-0.9; OR:0.7 95% CI:0.6-0.8). At the last visit, SLEDAI was significantly lower for subgroup 3 (OR:0.7 95% CI:0.5-0.9). Figure 2. Conclusions Patients with SLE from Norway can be grouped into 4 based on their antibody profile at the time of the diagnosis. Regardless of the limited sample size, the observations indicate that antibody-defined subgroups are a tool to reduce SLE heterogeneity and improve perspective to study this disease. References: [1.] Diaz-Gallo LM. ACR Open Rheumatol 2022;4(1):27-39.

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)0.001
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 score0.022
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.312
Teacher spread0.297 · 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".

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Citations0
Published2025
Admission routes1
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