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Record W4411846596 · doi:10.3899/jrheum.2025-0314.23

Clinical Features and Disease Phenotypes as Study Entry Criteria for Scleroderma Encompass Patients with Significant Biological Heterogeneity

2025· article· en· W4411846596 on OpenAlexaffvenue
Ka Lam Wong, Maximilien Lora, Qihuang Zhang, Melanie C. Baniña, Radhika Prabhune, Lucie Biard, Sabrina Hoa, Hongjun Wang, Gary Gilkeson, Dominique Farge, Inés Colmegna, Marie Hudson

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsJewish General HospitalMontreal Clinical Research InstituteCentre Hospitalier de l’Université de MontréalMcGill University
Fundersnot available
KeywordsMedicineScleroderma (fungus)DiseaseClinical phenotypeConnective tissue diseaseSystemic diseasePhenotypeAutoimmune diseaseDermatologyPathologyGenetics

Abstract

fetched live from OpenAlex

Objectives Clinical trials in systemic sclerosis (SSc) define strict eligibility criteria to overcome the known heterogeneity of the disease. We evaluated baseline circulating cytokines in participants of the Phase I/II Randomized Controlled Clinical Trial of Umbilical Cord-Derived Mesenchymal Stromal Cells in Systemic Sclerosis (CARE-SSc, NCT04356287 ) to verify this. Methods CARE-SSc is a randomized, double-blind, placebo-controlled trial testing the safety and generating preliminary evidence of the efficacy of umbilical cord-derived mesenchymal stromal cells in SSc. The inclusion criteria for CARE-SSc are adults who meet SSc criteria according to the ACR/EULAR 2013 classification criteria with: either a) disease duration of < 2 years with a modified Rodnan skin score (mRSS) > 20 and erythrocyte sedimentation rate > 25 mm and/or hemoglobin < 11 g/dL, or b) mRSS > 15 with at least 1 major organ involvement (lung, heart or kidneys); inadequate response or adverse effects with standard therapy; and, ineligibility or unwillingness to undergo autologous hematopoietic stem cell transplant. Baseline sera of the first 6 trial participants were analyzed with an extended cytokine/chemokine assay (Human Cytokine/ Chemokine 96-Plex Discovery Assay Array®, Eve Technologies). A non-clustered heatmap was generated using the Matplotlib coding library in Python. Results Five women and 1 man with median age of 46 (range 38-67) years, median disease duration of 3.5 (range 1-14) years and median mRSS of 18 (range 16-22) were included. Four subjects had lung disease and 4 had cardiac involvement. None had renal involvement. Five subjects had anti-topoisomerase I and 1 had anti-RNA polymerase III antibodies. At baseline, 5 patients were on mycophenolate, 2 on nintedanib and 3 on low-dose prednisone (< 10 mg/d). Among the 96 analytes, only 5 (IL-23, SCF, 6CKine, IL-17E, sCD40L) were consistently elevated among all 6 participants. Twenty-one analytes (TSLP, MIP-1β, MIG, MDC, TRAIL, RANTES, M-CSF, IL-27, TGFα, IL-15, IL-12p40, IL-10, PDGF-AA, IL-8, IL-7, IL-5, IL-3, IL-28A, TPO, FLT-3L, and IL-20) were within normal levels in all patients. Of the 3 components of the serum interferon inducible protein score tested (IP-10, MCP-2, MIG/CXCL9), only MCP-2 was above normal in 5 of 6 participants. Although individual pro-inflammatory cytokines were elevated in all participants, the patterns of elevation varied from patient to patient (Figure 1). Conclusion Selecting SSc patients based on clinical features results in varied cytokine endotypes. Whether this is associated with variability in treatment responses requires further investigation. If so, it could inform eligibility criteria in future SSc clinical trials.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.032
GPT teacher head0.339
Teacher spread0.307 · 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 routes2
Has abstractyes

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