Indicators of Functional Disability by Receipt of Disability Benefits Among Individuals With Systemic Lupus Erythematosus
Bibliographic record
Abstract
Objective We estimated the prevalence of potential functional disability among those with systemic lupus erythematosus (SLE), by receipt of disability benefits. Methods Participants (N = 443, mean age 46.2 years, 91.7% women, 82.6% Black) were recruited from a population-based SLE cohort. Indicators of potential disability included functioning impairments (Short Physical Performance Battery score ≤ 10; age-corrected National Institutes of Health Toolbox Cognition Battery composite score for fluid cognition < 77.5 [1.5 SD below the mean]); activity limitations (physical functioning T -scores < 35 [1.5 SD below the mean]); at least some difficulty performing ≥ 1 of the instrumental activities of daily living (IADLs) or basic activities of daily living (BADLs); and participation restrictions (any vs no reported effect of health on ability to work; restricted community mobility). We performed multivariable logistic regression models predicting potential disability indicators by self-reported receipt of disability benefits and then obtained adjusted prevalence estimates using postestimation margins. Results Those who reported receiving disability benefits (45.6%) vs not (54.4%) were more likely to have impairments in functioning (physical performance [71.3% vs 50%, P < 0.001]; fluid cognition [35.4% vs 19.2%, P = 0.01]), limitations in activities (self-reported physical limitations [26.7% vs 7.5%, P < 0.001]; IADLs [73.1% vs 42.9%, P < 0.001]; BADLs [60.6% vs 30.8%, P < 0.001]), and restrictions in participation (work [77.8% vs 60.6%, P = 0.09]; community mobility [43.1% vs 22%, P < 0.001]). These associations were not changed with adjustment for personal and SLE factors. Conclusion Receipt of disability benefits may be an incomplete marker of functioning. A substantial proportion of those not receiving benefits have impairments, limitations, and restrictions that should be addressed.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".