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RHUPUS SYNDROME: DESCRIPTION OF CLINICAL MANIFESTATIONS, ANALYTICAL FINDINGS, AND THERAPEUTIC APPROACH IN A SERIES OF 10 CASES

2025· article· en· W4410513017 on OpenAlexvenueno aff
Laura Salvador Maicas, J. J. Fragío Gil, Roxana González Mazarío, Amalia Rueda Cid, Pablo Martínez Calabuig, Mireia Lucía Sanmartín Martínez, Iván Jesús Lorente Betanzos, Juan J. Garrido, Clara Molina Almela, Antonio Sierra‐Rivera, Cristina Campos Fernández

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSeries (stratigraphy)Intensive care medicineDermatology

Abstract

fetched live from OpenAlex

PV145a / #751 Poster Topic: AS17 - Miscellaneous Background/Purpose Rhupus is a rare syndrome that combines characteristics of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Its prevalence is estimated to be 0.09%. It is characterized by erosive polyarthritis along with typical SLE symptoms and specific autoantibodies. The debate continues as to whether Rhupus is a true overlap of RA and SLE or represents a form of SLE with erosive joint involvement. In order to provide a better understanding of the topic, the objective of this study is to describe the demographic variables, analytical data, clinical manifestations, and therapeutic approaches in patients diagnosed with Rhupus. Methods A cross-sectional, monocentric study was conducted on patients diagnosed with Rhupus between January 2019 and December 2024. All patients met the ACR/EULAR 2010 criteria for RA and ACR/EULAR 2019 criteria for SLE. Demographic variables, analytical data (autoantibodies, cytopenias, acute phase reactants, and complement consumption), clinical manifestations, and the different treatments received during their evolution, including synthetic and biologic DMARDs, were collected Results Ten patients were included, 90% women, with a median age of 62 years. All received treatment with at least 3 DMARDs. The most commonly used synthetic DMARDs were methotrexate and hydroxychloroquine. Seven patients received biologic DMARDs, with rituximab being the most commonly used. One patient was treated with certolizumab due to fertility desires. Six patients received JAK inhibitors (4 with baricitinib and 2 with upadacitinib) (Table 1). All patients tested positive for antinuclear antibodies (ANA), 90% for anti-citrullinated peptide (anti-CCP), 80% for rheumatoid factor (RF), and 60% for anti-double-stranded DNA (anti-dsDNA). Other antibodies present can be seen in Table 2. The most frequent analytical alterations were elevated acute phase reactants (100%), lymphopenia (40%), and complement consumption (30%). Regarding clinical manifestations, arthritis was present in 100% of patients, 70% of which was erosive. Fatigue and morning stiffness affected 60%. Thirty percent showed typical SLE skin lesions such as malar rash, photosensitivity, and subacute cutaneous lupus. Other manifestations, such as alopecia and dry syndrome, were present in 30%. Less frequently, aphthosis, rheumatoid nodules, pericarditis, pericardial effusion, pleural effusion, and Raynaud’s phenomenon were observed (Table 2). TABLE 1. DEMOGRAPHIC CHARACTERISTICS AND TREATMENTS TABLE 2. ANALYTICAL DATA AND CLINICAL MANIFESTATIONS Conclusions All patients presented with difficult-to-manage symptoms, requiring treatment with at least 3 DMARDs. The most frequently used treatments were methotrexate, hydroxychloroquine, JAK inhibitors (tofacitinib and upadacitinib), and rituximab. The most common analytical findings were elevated acute phase reactants, lymphopenia, and complement consumption. All patients had positive ANA, 90% had positive anti-CCP, 80% had RF, and 60% had anti-dsDNA. Arthritis was the most frequent manifestation (100% of patients), with 70% of cases being erosive. Other typical SLE manifestations were present, with the most frequent being skin lesions in 30% of 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 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)0.000
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 score0.026
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.033
GPT teacher head0.310
Teacher spread0.276 · 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
Has abstractyes

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