GENERATING CANDIDATE DOMAINS FOR THE OMERACT SYSTEMIC LUPUS ERYTHEMATOSUS CORE OUTCOME SET
Bibliographic record
Abstract
PV223 / #525 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Systemic Lupus Erythematosus (SLE) is a chronic multisystemic heterogenous autoimmune disease that can present in patients through a myriad of different clinical symptoms. The complexity of SLE makes the impact on patients multifaceted through numerous domains hindering the standardization of the important domains to measure in clinical trials and longitudinal research. A Core Outcome Set (COS) can standardize the important domains of SLE and their measurement. In 1998, the first Outcome Measures in Rheumatology (OMERACT) SLE COS was developed, though it never progressed to instrument selection nor achieved patient representation. In 2018 we re-established the OMERACT SLE Working Group gathering collaborators (patients, clinicians, researchers, pharmaceutical representatives, and more representing 6 continents and over 260 members from over 35 countries) to develop a new SLE COS to address the unmet need of identifying the most important domains of SLE and standardizing their measurement. Methods Domain Generation We endeavored on 3 projects to identify candidate domains for the SLE COS. The first project was a survey of domains revisiting known SLE domains and identifying novel domains. The survey was administered to 100 SLE patients from the University of Toronto Lupus Clinic and 145 OMERACT SLE Working Group members. The second project was a scoping literature review of SLE systematic reviews and clinical trials since 2010 capturing domains, definitions, and measurement instruments. The third project was focus groups with SLE patients from around the world identifying the impactful and important domains to SLE patients. Domain Winnowing and Binning Preliminary domains identified from domain generation were reviewed by the OMERACT SLE Advisory Group. Domains deemed too contextual, narrow, broad, or unspecific were winnowed out and the remainder were binned into appropriate domains. Domain Definition Candidate definitions were identified from the scoping literature review and additional searches of literature looking at published definitions. The OMERACT SLE Advisory Group reviewed candidate definitions, modified definitions if required, and selected suitable definitions for each candidate domain. Results Domain generation, winnowing, and binning identified 25 candidate domains and definitions for each candidate domains were established (Table 1). Table 1. Candidate Domains and Definitions Conclusions Candidate domains with definitions have been prepared for the SLE COS. The proceeding stage of COS development will be a Delphi consensus exercise beginning in January, 20225, where patients and other collaborators from around the world will participate to vote on the most important domains of SLE to capture in all SLE clinical trials and longitudinal research. The Delphi will identify the core domains that will make up the SLE COS. Measurement instruments for each core domain will be identified and appraised on their measurement properties, and the most suitable will be selected to capture each core domain. The work to modify or develop a novel instrument should no suitable one be identified will be recommended if required. The final product will be a new SLE COS able to standardize the measurement and reporting of important domains of SLE in clinical trials and longitudinal research. The SLE COS will provide standardized methodology and terminology to capture and report domains, prevent duplicate and waste research, create a trove of more accessible and interpretable data to advance research, and support regulatory bodies with endorsing pharmaceutical interventions assessed using the OMERACT SLE COS.
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 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 | 0.025 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".