Prediction of Hospitalizations 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) predicts mortality and damage accrual in systemic lupus erythematosus (SLE), but its association with hospitalizations has not been described. Our objective was to estimate the association of baseline SLICC-FI values with future hospitalizations in the SLICC inception cohort. METHODS: Baseline SLICC-FI scores were calculated. The number and duration of inpatient hospitalizations during follow-up were recorded. Negative binomial regression was used to estimate the association between baseline SLICC-FI values and the rate of hospitalizations per patient-year of follow-up. Linear regression was used to estimate the association of baseline SLICC-FI scores with the proportion of follow-up time spent in the hospital. Multivariable models were adjusted for relevant baseline characteristics. RESULTS: The 1,549 patients with SLE 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) at baseline. Mean ± SD baseline SLICC-FI was 0.17 ± 0.08. During mean ± SD follow-up of 7.2 ± 3.7 years, 614 patients (39.6%) experienced 1,570 hospitalizations. Higher baseline SLICC-FI values (per 0.05 increment) were associated with more frequent hospitalizations during follow-up, with an incidence rate ratio of 1.21 (95% confidence interval [95% CI] 1.13-1.30) after adjustment for baseline age, sex, glucocorticoid use, immunosuppressive use, ethnicity/location, SLE Disease Activity Index 2000 score, SLICC/American College of Rheumatology Damage Index score, and disease duration. Among patients with ≥1 hospitalization, higher baseline SLICC-FI values predicted a greater proportion of follow-up time spent hospitalized (relative rate 1.09 [95% CI 1.02-1.16]). CONCLUSION: The SLICC-FI predicts future hospitalizations among incident SLE patients, further supporting the SLICC-FI as a valid health measure in 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".