Association of the Systemic Lupus International Collaborating Clinics Frailty Index and Damage Accrual in Longstanding Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To externally validate the Systemic Lupus International Collaborating Clinics Frailty Index (SLICC-FI) in a prevalent systemic lupus erythematosus (SLE) cohort and to assess the ability of the SLICC-FI to predict organ damage accrual among individuals with longstanding SLE. METHODS: This was a secondary analysis of data from the Study of Lupus Vascular and Bone Long-Term Endpoints (SOLVABLE) cohort, which consists of adult women from the Chicago Lupus Database who met the 1997 revised American College of Rheumatology (ACR) classification criteria for SLE. There were 185 patients with SLE enrolled, of whom 149 patients were included in a 5-year follow-up analysis. The SLICC-FI and SLICC/ACR Damage Index (SDI) scores were calculated at baseline and 5-year follow-up. Unadjusted and adjusted logistic regression models estimated the association of baseline SLICC-FI scores (per 0.05 increase) with damage accrual at 5-year follow-up. RESULTS: At enrollment the mean ± SD age of the 149 patients was 43.30 ± 10.15 years, the mean ± SD disease duration was 11.93 ± 8.46 years, and the mean ± SD SDI score was 1.64 ± 1.83. At baseline, the mean ± SD SLICC-FI score was 0.18 ± 0.08, and 36% of participants were categorized as frail (SLICC-FI score >0.21). In a model adjusted for age, race, and disease duration, each 0.05-unit increase in the baseline SLICC-FI score was associated with 28% higher odds of subsequent damage accrual (odds ratio 1.28, 95% confidence interval 1.01-1.63). CONCLUSION: In a prevalent cohort of women with established SLE, higher baseline SLICC-FI scores were associated with a higher risk of subsequent damage accrual at 5-year follow-up.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".