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FEASIBILITY AND USABILITY OF A CLINICAL DECISION SUPPORT SYSTEM FOR TREAT-TO-TARGET IN SYSTEMIC LUPUS ERYTHEMATOSUS: THE T2T-SLE PILOT STUDY

2025· article· en· W4410715582 on OpenAlexvenueno aff
Agner Parra Sanchez, Koen Vos, Odile van Hall, Irene E. M. Bultink, Michel Tsang-A-Sjoe, Alexandre E. Voskuyl, Ronald Van Vollenhoven

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineUsabilityPhysical therapyLupus erythematosusSystemic therapyInternal medicineImmunologyAntibodyHuman–computer interaction

Abstract

fetched live from OpenAlex

PT015 / #768 Topic: AS24 - SLE-Treatment POSTER TOUR 03: RECENT ADVANCEMENTS IN SLE CLINICAL OUTCOMES AND THERAPY 23-05-2025 10:00 AM - 10:40 AM Background/Purpose The Treat-to-Target (T2T) strategy has been endorsed for systemic lupus erythematosus (SLE) management, yet its implementation remains challenging.[1] A clinical decision support system (CDSS) was developed to assist physicians in applying a T2T approach in clinical practice.[2] This study aimed to assess the feasibility, usability, and acceptability of a CDSS designed to implement a T2T strategy in SLE management from the perspectives of both physicians and patients. Methods The T2T-SLE study was a 24-week, nonrandomized, cluster, multicenter pilot study conducted in North Holland, including 2 tertiary university medical centers (UMCs) and 2 regional outpatient clinics. Patients diagnosed with SLE were assigned by treatment center to either routine care or T2T-CDSS-assisted care. The CDSS, developed based on evidence-based guidelines, provided clinical recommendations for disease management. Primary outcomes included feasibility (recruitment, retention, and implementation challenges), usability (physicians perceived ease of use of the CDSS web-app), and acceptability (physician and patient satisfaction). The CDSS was evaluated solely by physicians, with 1 physician in an UMC and 1 in a regional center using the tool. In turn, patients reported their satisfaction with the T2T strategy, which involved more frequent outpatient clinic visits and structured discussions on treatment targets. Patient feedback was collected through qualitative questionnaires and patient-reported outcome measures (PROMs). Secondary outcomes included treatment patterns, disease activity measures, and implementation barriers and facilitators. Results A total of 91 participants were screened, with 38 enrolled (41.8%) and 35 completing the study (92.1% retention). The most common reason for declining participation was unspecified reasons (24.7%) followed by lack of interest (23.1%) and scheduling conflicts due to life events (4.4%) (Figure 1). The enrolled population was representative of a broad spectrum of SLE severity, with a mix of stable and active disease. Patients in the T2T-CDSS group had slightly lower baseline disease activity scores compared to the routine care group, though both groups exhibited similar demographic characteristics (Table 1). Physicians reported that the CDSS was useful in supporting T2T-based decision-making, but challenges related to workflow integration and time constraints were noted. Patients in the T2T group generally expressed satisfaction with the strategy, highlighting the benefits of increased monitoring and shared treatment goal discussions. However, some reported concerns about the burden of more frequent visits. Fig 1. Enrollment Diagram, Inclusion and Exclusion Criteria Table 1. Baseline characteristics in patients from the treat-to-target and the routine care group Conclusions The T2T-SLE pilot study demonstrated the feasibility of using a CDSS to implement a T2T strategy in SLE management. Physician-reported usability was positive, though workflow integration challenges were noted. Patients valued structured treatment discussions but reported mixed opinions on visit frequency. The study demonstrated feasibility for larger-scale implementation, though recruitment delays and engagement challenges indicate a need for improved patient outreach strategies. Future studies should optimize recruitment strategies and further assess long-term clinical effectiveness. References: [1.] Parra Sanchez AR. Nat Rev Rheumatol 2022;18(3):146-57. [2.] Parra Sanchez AR. BMJ Health Care Inform 2023;30(1):e100811.

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)0.025
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.068
GPT teacher head0.400
Teacher spread0.332 · 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 designNon-randomized trial
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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