Construction of a Frailty Index as a Novel Health Measure in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To construct a Frailty Index (FI) as a measure of vulnerability to adverse outcomes among patients with systemic lupus erythematosus (SLE), using data from the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort. METHODS: The SLICC inception cohort consists of recently diagnosed patients with SLE followed annually with clinical and laboratory assessments. For this analysis, the baseline visit was defined as the first study visit at which sufficient information was available for construction of an FI. Following a standard procedure, variables from the SLICC database were evaluated as potential health deficits. Selected health deficits were then used to generate a SLICC-FI. The prevalence of frailty in the baseline dataset was evaluated using established cutpoints for FI values. RESULTS: The 1683 patients with SLE (92.1% of the overall cohort) eligible for inclusion in the baseline dataset were mostly female (89%) with mean (SD) age 35.7 (13.4) years and mean (SD) disease duration 18.8 (15.7) months at baseline. Of 222 variables, 48 met criteria for inclusion in the SLICC-FI. Mean (SD) SLICC-FI was 0.17 (0.08) with a range from 0 to 0.51. At baseline, 27.1% (95% CI 25.0-29.2) of patients were classified as frail, based on SLICC-FI values > 0.21. CONCLUSION: The SLICC inception cohort permits feasible construction of an FI for use in patients with SLE. Even in a relatively young cohort of patients with SLE, frailty was common. The SLICC-FI may be a useful tool for identifying patients with SLE who are most vulnerable to adverse outcomes, but validation of this index is required prior to its use.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".