LO-031 Working towards a core minimal dataset for systemic lupus erythematosus studies
Bibliographic record
Abstract
Background Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease and its heterogeneity means that many cohorts and clinical trials do not collect the same data, making comparative studies difficult. We aimed to ascertain what data is commonly collected in SLE registries/trials. Our ultimate goal is to generate a core minimal dataset for future SLE studies. Methods We designed and distributed a questionnaire to members of the Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) as well as additional research active centres in China. Our survey included 26 questions about the types of data that are routinely collected for research. Data collected by ≥ 75% of participating centres was used as a threshold for concordance. Results Eighteen centres; eight from USA/Canada, five from China and five from Europe responded. Table 1 shows the data that ≥ 75% centres collected. Notably, the EULAR/ACR 2019 criteria was collected by 33.3% and even the most commonly collected patient reported outcome (PRO) – the SF36, was only collected by 53.3%. The SLICC/ACR SLE damage index did not reach our threshold (68.4%). Current treatment data collected did include drug name, dose, frequency and start date. Previous treatment reason for cessation was collected only pertaining to antimalarials. Baseline anti-dsDNA (ELISA) and Crithidia were collected by 70.6% and 52.9% respectively and baseline immunoglobulins were measured by 70.6%. Follow-up blood counts and complement C3/4 were collected by ≥ 75% of centres. Conclusions An initial set of commonly collected data was identified. This forms the basis to start to consider a core minimum dataset for SLE. Development and further refinement of this dataset is needed, as will common definitions of each data field to allow 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 | 0.130 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".