Prediction of Damage Accrual in Systemic Lupus Erythematosus Using the Systemic Lupus International Collaborating Clinics Frailty Index
Bibliographic record
Abstract
OBJECTIVE: The Systemic Lupus International Collaborating Clinics (SLICC) frailty index (FI) has been shown to predict mortality, but its association with other important outcomes is unknown. We examined the association of baseline SLICC FI values with damage accrual in the SLICC inception cohort. METHODS: The baseline visit was defined as the first visit at which both organ damage (SLICC/American College of Rheumatology Damage Index [SDI]) and health-related quality of life (Short Form 36) were assessed. Baseline SLICC FI scores were calculated. Damage accrual was measured by the increase in SDI between the baseline assessment and the last study visit. Multivariable negative binomial regression was used to estimate the association between baseline SLICC FI values and the rate of increase in the SDI during follow-up, adjusting for relevant demographic and clinical characteristics. RESULTS: The 1,549 systemic lupus erythematosus (SLE) patients eligible for this analysis were mostly female (88.7%) with a mean ± SD age of 35.7 ± 13.3 years and a median disease duration of 1.2 years (interquartile range 0.9-1.5 years) at baseline. The mean ± SD baseline SLICC FI was 0.17 ± 0.08. Over a mean ± SD follow-up of 7.2 ± 3.7 years, 653 patients (42.2%) had an increase in SDI. Higher baseline SLICC FI values (per 0.05 increase) were associated with higher rates of increase in the SDI during follow-up (incidence rate ratio [IRR] 1.19 [95% confidence interval 1.13-1.25]), after adjusting for age, sex, ethnicity/region, education, baseline SLE Disease Activity Index 2000, baseline SDI, and baseline use of glucocorticoids, antimalarials, and immunosuppressive agents. CONCLUSION: Our findings indicate that the SLICC FI predicts damage accrual in incident SLE, which further supports the SLICC FI as a valid health measure in SLE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".