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Record W4411884402 · doi:10.3899/jrheum.2025-0314.62

Comparing Dermatologist and Rheumatologist Perspectives: Insights from the Quest (Quinacrine for Systemic Lupus Treatment) Survey

2025· article· en· W4411884402 on OpenAlexaffvenueabout
Sarah Aly, Gilda Parastandehchehr, Évelyne Vinet, N. Costedoat‐Chalumeau, Paul R. Fortin, John P. Reynolds, Carter Thorne, Zahi Touma, Daniel F. Wallace, Victoria P. Werth, Sasha Bernatsky, Arielle Mendel

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsToronto Western HospitalUniversité LavalCentre hospitalier de l'Université LavalMcGill UniversityArthritis Research Centre of CanadaMcGill University Health Centre
Fundersnot available
KeywordsMedicineDiscontinuationHydroxychloroquineSystemic lupus erythematosusRheumatologyMedical prescriptionAdverse effectInternal medicineLupus erythematosusDiseaseDermatologyFamily medicineImmunologyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Objectives To evaluate the experience with quinacrine for the treatment of cutaneous lupus among dermatologists with expertise in rheumatic disease, and compare this to the experience of rheumatology-based systemic lupus erythematosus (SLE) experts. Methods In November 2023, we conducted an electronic survey among members of the Rheumatologic Dermatology Society (RDS, n=20 dermatologists with rheumatic disease interest) and compared responses to those from April and August 2023 surveys of Systemic Lupus International Collaborating Clinics and Canadian Network for Improved Outcomes in Systemic Lupus Erythematosus (SLICC, CaNIOS, n=40) physicians. Participants provided information on quinacrine availability, prescribing practices, and perceived effectiveness and safety. Results All RDS respondents were from the United States (US), while SLICC+CaNIOS respondents were geographically diverse (65% North America, 25% Europe, 3% South America, 8% Asia). Quinacrine prescription rates differed markedly: 100% of RDS vs 43% of SLICC/CaNIOS had prescribed it for lupus. All RDS respondents had used quinacrine in combination with another antimalarial, versus 71% of those who had prescribed quinacrine in the SLICC+CaNIOS group. Primary reasons for prescribing were similar: hydroxychloroquine/chloroquine intolerance (50% RDS, 71% SLICC+CaNIOS) and as an alternative following retinal toxicity (40% RDS, 47% SLICC+CaNIOS). Clinical benefit (in at least 1 patient) was reported by 95% of RDS and 71% of SLICC+CaNIOS respondents. Discontinuation rates varied: 30% of RDS reported no discontinuations, while all SLICC+CaNIOS reported at least 1 discontinuation, mainly due to lack of efficacy (59%) and/or adverse effects (59%). Conversely, RDS respondents primarily cited loss of availability (50%) as the reason for discontinuation. Future prescribing intentions differed: 95% of RDS would consider quinacrine for refractory cutaneous lupus if available, compared to 13% of SLICC+CaNIOS members. Key prescribing barriers were consistently identified as lack of availability (100% RDS, 88% SLICC+CaNIOS) and cost (60% RDS, 23% SLICC+CaNIOS). Conclusion We observed differences in experiences with quinacrine for lupus between dermatologists and rheumatologists. US-based dermatologists reported higher prescription rates, better perceived effectiveness, and fewer discontinuations. Some differences may reflect differences in quinacrine availability between groups, and both groups identified availability as the primary barrier for prescription. These findings suggest the need for interdisciplinary collaboration to optimize quinacrine use in lupus and underscore the importance of continued research to establish quinacrine’s role in managing cutaneous and systemic lupus manifestations.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.044
GPT teacher head0.327
Teacher spread0.284 · 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 designQualitative
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 routes3
Has abstractyes

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