Disease-specific quality of life in patients with lupus nephritis
Bibliographic record
Abstract
Background Patient-reported outcomes in lupus nephritis (LN) are not well studied. Studies with disease-targeted PRO tool in LN do not exist. Herein, we describe quality of life (QOL: HRQOL & non-HRQOL) among LN patients using LupusPRO. Methods International, cross-sectional data from 1259 patients with systemic lupus erythematosus (SLE) and LupusPRO were compared, stratified by (a) presence of LN (ACR classification criteria (ACR-LN)) at any time and, (b) active LN (on SLEDAI) at study visit. Damage was assessed by SLICC/ACR-SDI. Multivariate regression analyses for QOL against ACR-LN (active LN) after adjusting for age, gender, ethnicity and country of recruitment were performed. Results Mean (SD) age was 41.7 (13.5) yrs, 93% were women. Five hundred and thirty-nine of 1259 SLE patients had ACR-LN. ACR-LN group was younger, were more often on immunosuppressive medications, had worse QOL on lupus medications and procreation than non-ACR-LN patients. HRQOL and non-HRQOL scores were similar in both groups. One hundred and twenty-nine of 539 ACR-LN patients had active LN. Active LN group was younger, had greater disease activity and had worse HRQOL and non-HRQOL compared to patients without active LN. Specific domains adversely affected were lupus symptoms, lupus medications, procreation, emotional health, body image and desires-goals domains. Patients with ACR-LN and active LN fared significantly worse in lupus medications and procreation HRQOL domains, even after adjusting for age, ethnicity, gender and country of recruitment. Conclusions Lupus nephritis patients have poor QOL. Patients with active LN have worse HRQOL and non-HRQOL. Most domains affected are not included in the generic QOL tools used in SLE. LN patients must receive discussion on lupus medications and procreation issues. Patients with active LN need comprehensive assessments and addressal of QOL, along with treatment for active LN.
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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 | 0.003 |
| 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".