Association of Patient Resilience With Patient-Reported Physical and Mental/Emotional Quality of Life in Systemic Lupus Erythematosus
Bibliographic record
Abstract
Objective To examine the association of patient resilience with health-related quality of life (HRQOL) in systemic lupus erythematosus (SLE). Methods We used data from a prospective cohort study of patients with SLE enrolled across 15 rheumatology clinics across the US who viewed a patient decision aid for SLE management during a regular clinic visit. We examined the association of high resilience with HRQOL on 29-item Patient Reported Outcomes Measurement Information System (PROMIS-29) domains using multivariable linear mixed-effects model analysis, adjusted for demographics, social determinants of health (SDOH), flare, site, time, and comorbid rheumatic diseases. Results Out of 945 patients with SLE, 27% had high resilience, with a 2-item Connor-Davidson Resilience Scale (CD-RISC2) score of 8. Compared to patients who had low resilience, those with high resilience were more likely to report excellent or very good health (46% vs 17.7%); lower SLE activity (3.94 vs 5.18 on a 0-10 scale), and higher SLE wellness (7.54 vs 6.15 on a 0-10 scale). We noted that high resilience was associated with a positive moderate effect size (Cohen d > 0.5) for physical functioning, social participation, emotional anxiety, emotional distress, fatigue, pain interference, and pain intensity, as well as a favorable small effect size (Cohen d 0.2-0.49) for sleep disturbance, in unadjusted analyses. In a multivariable-adjusted mixed linear regression analysis, high resilience was associated with all 8 HRQOL scale scores. Conclusion High patient resilience was independently associated with better HRQOL outcomes in SLE after adjusting for demographics, SDOH, SLE flare, site, time, and comorbid rheumatic diseases. Interventions to promote resilience have the potential to improve HRQOL outcomes in SLE. ( ClinicalTrials.gov : NCT03735238 )
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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 | 0.010 |
| 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 | 0.001 |
| 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".