Scoping literature review to identify candidate domains for the OMERACT Systemic Lupus Erythematosus core outcome set
Bibliographic record
Abstract
OBJECTIVE: To identify candidate Systemic Lupus Erythematosus (SLE) domains from the literature for consideration towards the development of the SLE Core Outcome Set. METHODS: This was a comprehensive scoping literature review of SLE clinical trials and systematic reviews published since 2010. Studies were identified from 5 databases and were screened for eligibility. Candidate domains were extracted from the included studies. Candidate domains were winnowed and binned by the Outcome Measures in Rheumatology (OMERACT) SLE Advisory Group. RESULTS: Of the 4063 studies identified, 507 met inclusion criteria and proceeded to data extraction. Multiple domains and items were extracted, which winnowing and binning reduced to 25 candidate domains. CONCLUSION: The 25 candidate domains cover the important aspects of SLE and the 4 core areas of disease impact according to OMERACT framework. The 25 candidate domains constitute a feasible and manageable number of domains to proceed with to the core domain consensus stage that covers the wide range of impact of SLE. The candidate domains will be supplemented by ongoing qualitative research with patients living with SLE to identify additional domains before proceeding to the consensus stage.
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 | 0.087 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.053 | 0.034 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".