Evaluating the Construct of Damage in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: The Systemic Lupus International Collaborating Clinics (SLICC), American College of Rheumatology (ACR), and the Lupus Foundation of America are developing a revised systemic lupus erythematosus (SLE) damage index (the SLICC/ACR Damage Index [SDI]). Shifts in the concept of damage in SLE have occurred with new insights into disease manifestations, diagnostics, and therapy. We evaluated contemporary constructs in SLE damage to inform development of the revised SDI. METHODS: We conducted a 3-part qualitative study of international SLE experts. Facilitated small groups evaluated the construct underlying the concept of damage in SLE. A consensus meeting using nominal group technique was conducted to achieve agreement on aspects of the conceptual framework and scope of the revised damage index. The framework was finally reviewed and agreed upon by the entire group. RESULTS: Fifty participants from 13 countries were included. The 8 thematic clusters underlying the construct of SLE damage were purpose, items, weighting, reversibility, impact, time frame, attribution, and perspective. The revised SDI will be a discriminative index to measure morbidity in SLE, independent of activity or impact on the patient, and should be related to mortality. The SDI is primarily intended for research purposes and should take a life-course approach. Damage can occur before a diagnosis of SLE but should be attributable to SLE. Damage to an organ is irreversible, but the functional consequences on that organ may improve over time through physiological adaptation or treatment. CONCLUSION: We identified shifts in the paradigm of SLE damage and developed a unifying conceptual framework. These data form the groundwork for the next phases of SDI development.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".