Towards a minimal core dataset for systemic lupus erythematosus studies
Bibliographic record
Abstract
OBJECTIVE: SLE is a complex, heterogenous autoimmune disease. SLE researchers do not always collect the same data, making comparative studies difficult. We aimed to ascertain what variables SLE clinical researchers commonly collect for SLE research. Our ultimate goal is to generate a minimal core dataset for future SLE studies. METHODS: In 2020, we designed and distributed a questionnaire to members of the Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) as well as additional active research centres in China. Our survey included 26 questions about the types of data that are routinely collected for research. Variables collected by ≥75% of participating respondents were used as a threshold for inclusion. RESULTS: 18 of 36 invited respondents replied (8 from USA/Canada, 5 from China and 5 from Europe). Many key variables in the domains of sociodemographics, SLE specific, comorbidities, baseline haematology/biochemistry/immunology and treatment data were collected by ≥75% respondents including the 1997 American College of Rheumatology (ACR) Classification Criteria (83%), SLE Disease Activity Index-2000 (82%), current treatment (100%), drug name, dose, frequency and start date (75-100%) and complement C3/4 (94%). A range of other items was collected by 50-<75% of respondents including SLICC 2012 Criteria (67%), SLICC/ACR Damage Index (68%) and Short Form Health Survey-36 (53%). Less than 50% of respondents collect certain items including European Alliance of Associations for Rheumatology/ACR 2019 criteria (33%), British Isles Lupus Assessment Group scores (12%) and pneumococcal vaccine status (39%). CONCLUSIONS: The frequency with which an initial set of variables is collected in SLE cohorts globally was identified and can form the basis from which to develop a core minimum dataset for SLE. Further refinement and common definitions will be needed to finalise a minimal core dataset suitable for widespread use.
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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 | 0.096 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".