Comparison of two frailty definitions in women with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: Frailty is a risk factor for adverse health in SLE. The Fried phenotype (FP) and the SLICC Frailty Index (SLICC-FI) are common frailty metrics reflecting distinct approaches to frailty assessment. We aimed to (1) compare frailty prevalence according to both metrics in women with SLE and describe differences between frail and non-frail participants using each method and (2) evaluate for cross-sectional associations between each metric and self-reported disability. METHODS: Women aged 18-70 years with SLE were enrolled. FP and SLICC-FI were measured, and agreement calculated using a kappa statistic. Physician-reported disease activity and damage, Patient Reported Outcome Measurement Information System (PROMIS) computerized adaptive tests, and Valued Life Activities (VLA) self-reported disability were assessed. Differences between frail and non-frail participants were evaluated cross-sectionally, and the association of frailty with disability was determined for both metrics. RESULTS: Of 67 participants, 17.9% (FP) and 26.9% (SLICC-FI) were frail according to each metric (kappa = 0.41, P < 0.01). Compared with non-frail women, frail women had greater disease damage, worse PROMIS scores, and greater disability (all P < 0.01 for FP and SLICC-FI). After age adjustment, frailty remained associated with a greater odds of disability [FP: odds ratio (OR) 4.7, 95% CI 1.2, 18.8; SLICC-FI: OR 4.6, 95% CI 1.3, 15.8]. CONCLUSION: Frailty is present in 17.9-26.9% of women with SLE. These metrics identified a similar, but non-identical group of women as frail. Further studies are needed to explore which metric is most informative in this population.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".