External validation of the Systemic Lupus International Collaborating Clinics Frailty Index as a predictor of adverse health outcomes in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: The SLICC frailty index (SLICC-FI) was recently developed as a measure of susceptibility to adverse outcomes in SLE. We aimed to externally validate the SLICC-FI in a prevalent cohort of individuals with more long-standing SLE. METHODS: This secondary analysis included data from a single-centre prospective cohort of adult patients with established SLE (disease duration >15 months at enrolment). The baseline visit was the first at which both SLICC/ACR Damage Index (SDI) and 36-item Short Form data were available. Baseline SLICC-FI scores were calculated. Cox regression models estimated the association between baseline SLICC-FI values and mortality risk. Negative binomial regression models estimated the association of baseline SLICC-FI scores with the rate of change in SDI scores during follow-up. RESULTS: The 183 eligible SLE patients were mostly female (89%) with a mean age of 45.2 years (s.d. 13.2) and a median disease duration of 12.4 years (interquartile range 7.8-17.4) at baseline. The mean baseline SLICC-FI score was 0.17 (s.d. 0.09), with 54 patients (29.5%) classified as frail (SLICC-FI >0.21). Higher baseline SLICC-FI values (per 0.05 increase) were associated with an increased mortality risk [hazard ratio 1.31 (95% CI 1.01, 1.70)] after adjusting for age, sex, education, SLE medication use, disease duration, smoking status and baseline SDI. Higher baseline SLICC-FI values (per 0.05 increase) were associated with increased damage accrual over time [incidence rate ratio 1.18 (95% CI 1.07, 1.29)] after adjusting for potential confounders. CONCLUSION: Frailty, measured using the SLICC-FI, predicts organ damage accrual and mortality risk among individuals with established SLE.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".