Evaluating the Properties of a Frailty Index and Its Association With Mortality Risk Among Patients With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To evaluate the properties of a frailty index (FI), constructed using data from the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort, as a novel health measure in systemic lupus erythematosus (SLE). METHODS: For this secondary analysis, the baseline visit was defined as the first study visit at which both organ damage (SLICC/American College of Rheumatology Damage Index [SDI]) and health-related quality of life (Short-Form 36 [SF-36] scores) were assessed. The SLICC-FI was constructed using baseline data. The SLICC-FI comprises 48 health deficits, including items related to organ damage, disease activity, comorbidities, and functional status. Content, construct, and criterion validity of the SLICC-FI were assessed. Multivariable Cox regression was used to estimate the association between baseline SLICC-FI values and mortality risk, adjusting for demographic and clinical factors. RESULTS: In the baseline data set of 1,683 patients with SLE, 89% were female, the mean ± SD age was 35.7 ± 13.4 years, and the mean ± SD disease duration was 18.8 ± 15.7 months. At baseline, the mean ± SD SLICC-FI score was 0.17 ± 0.08 (range 0-0.51). Baseline SLICC-FI values exhibited the expected measurement properties and were weakly correlated with baseline SDI scores (r = 0.26, P < 0.0001). Higher baseline SLICC-FI values (per 0.05 increment) were associated with increased mortality risk (hazard ratio 1.59, 95% confidence interval 1.35-1.87), after adjusting for age, sex, steroid use, ethnicity/region, and baseline SDI scores. CONCLUSION: The SLICC-FI demonstrates internal validity as a health measure in SLE and might be used to predict future mortality risk. The SLICC-FI is potentially valuable for quantifying vulnerability among patients with SLE, and adds to existing prognostic scores.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".