Evaluation of a Patient-reported Frailty Tool in Women With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Frailty is associated with mortality in systemic lupus erythematosus (SLE), but how best to measure frailty is unclear. We aimed to compare 2 frailty metrics, the self-reported Fatigue, Resistance, Ambulation, Illnesses, and Loss of weight (FRAIL) scale (FS) and the Fried phenotype (FP), in SLE to evaluate differences between frail and nonfrail women and whether frailty is associated with self-reported disability. METHODS: Adult women aged < 70 years with validated SLE and mild/moderate disease enrolled in this cross-sectional study between August 2018 and October 2019. Correlation and agreement between the FS and the FP were determined. Differences in sociodemographic and disease characteristics, patient-reported outcome measures (PROMs), and biomarkers between frail and nonfrail participants were evaluated, as well as the association of frailty with Valued Life Activities disability. RESULTS: = 0.0004) between the FS and the FP were significant. Frail women had greater disease damage, high-sensitivity C-reactive protein, and interleukin 6, and worse PROMs according to both frailty definitions. Both frailty measures were associated with self-reported disability after adjustment for age, comorbidity, and disease activity and damage; this relationship was attenuated for the FP. CONCLUSION: Frailty prevalence was high in this cohort of women with SLE using both frailty definitions, suggesting that frailty may be accelerated in women with SLE, particularly when based exclusively on self-report. Frailty remained associated with self-reported disability in adjusted analyses. The FS may be an informative point-of-care tool to identify frail women with 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| 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".