EULAR RECOMMENDATIONS FOR A CORE DATA SET TO SUPPORT CLINICAL CARE AND TRANSLATIONAL AND OBSERVATIONAL RESEARCH IN SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
PT004 / #704 Topic: AS13 - Guidelines and Recommendations POSTER TOUR 01: CLINICAL OUTCOMES IN SLE 22-05-2025 10:00 AM - 10:40 AM Background/Purpose To enhance clinical and multicenter research outcomes in systemic lupus erythematosus (SLE), standardized documentation of patient- and disease-related features is important. The aim of this EULAR taskforce was to define a core set of essential items for the comprehensive care of SLE patients in clinical practice, with an extension for vital elements required for translational and observational research. Methods A multidisciplinary EULAR task force group engaged in a multistep approach including a 4 round Delphi survey and a face-to-face meeting. Results Twenty-five stakeholders from 14 different countries participated. During the process, the initial list of 99 items was reduced to 73 items for inclusion in the clinical core data set and 8 additional items for research extension. The items were grouped in the domains ‘general,’ ‘disease activity,’ ‘disease history,’ ‘disease damage,’ ‘comorbidities,’ ‘patient-reported outcomes,’ ‘laboratory markers,’ ‘outcomes,’ and ‘treatment,’ with suggested frequencies of assessment (Figure 1). Figure 1. Core Data Set for SLE to support clinical care and research extension for observational and translational research (research extension in red) Conclusions The presented clinical core data set and its research extension are designed to improve SLE patient care and facilitate collaborative research by ensuring the comparability of datasets and cohort descriptions. This initiative lays the foundation for the establishment of a global SLE data space, and has the potential to expedite the implementation of personalized medicine in SLE care. This work was funded by a EULAR grant and was submitted on behalf of Taskforce for development of EULAR recommendations for a core data set to support clinical care and translational and observational research in systemic lupus erythematosus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.628 | 0.588 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.016 | 0.023 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".