THE SYSTEMIC LUPUS INTERNATIONAL COLLABORATING CLINICS (SLICC) FRAILTY INDEX (SLICC-FI) PREDICTS HOSPITALIZATIONS IN A PREVALENT LATIN AMERICAN LUPUS COHORT.
Bibliographic record
Abstract
PV210 / #497 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Frailty, measured with the SLICC-FI, has been reported as a predictor of damage in several cohorts. The aim of this study is to evaluate the SLICC-FI as a predictor of hospitalization in systemic lupus erythematosus (SLE) patients. Methods Patients from a single-center prevalent cohort were included. The SLICC-FI was measured at baseline. Hospitalizations were reported during the first 2 years after the baseline visit as their number as well as their duration. Univariable and multivariable negative binomial regressions were performed to determine the association between the baseline SLICC-FI (per 0.05 increase) and hospitalizations during follow-up (number and length), adjusted for sex, age at diagnosis, socioeconomic status, educational level, disease duration, SLE Disease Activity Index 2000 (SLEDAI-2K), SLICC damage index (SDI), prednisone daily dose, antimalarial and immunosuppressive drug use at baseline. Results Of the 302 patients included 280 (92.7%) were female, with mean (SD) age at diagnosis of 34.6 (7.2) years. At baseline, the mean (SD) disease duration was 7.2 (6.4) years, and the mean (SD) SLICC-FI was 0.21 (0.05). The mean number of hospitalizations per patient was 0.5 (1.6) and the mean number of days hospitalized during the two-year period per patient was 5.3 (16.8) days; 58 (17.9%) of the patients were hospitalized at least once during the follow-up. The SLICC-FI predicted a higher probability of hospitalizations as well as with a higher number of hospitalizations; these data are depicted in Table 1. Table 1: Impact of the SLICC-FI on the number and length of hospitalizations. Univariable and multivariable analyses Conclusions The SLICC-FI predicts hospitalizations in SLE patients, independently of other well-known risk factors of hospitalizations. Further studies are needed to determine strategies to improve frailty in SLE patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".