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 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".