Generic Versus Disease-specific Measures of Health-related Quality of Life in Systemic Lupus Erythematosus
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disease that significantly affects health-related quality of life (HRQOL): physical, psychologic, mental, and social aspects of well-being that are influenced by disease, in the context of life experiences and expectations specific to each patient1. A relapsing, remitting chronic disease, SLE results in disability in 20%–40% of afflicted young and middle-aged women and men2. In patients with SLE, HRQOL is influenced by disease activity and symptoms of fatigue, depression, pain, sleep disturbances, and cognitive dysfunction3. Across 5 randomized controlled trials (RCT) in SLE, baseline HRQOL scores were low and were similar to those of subjects following myocardial infarction or with chronic congestive heart failure4. Lower scores were highly correlated with history of renal disease, presence of anti-dsDNA antibodies, higher disease activity scores by Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and/or Safety of Estrogens in Lupus Erythematosus National Assessment (SELENA-SLEDAI), hypocomplementemia, African American descent, and age. A variety of therapeutic interventions, including pharmacologic and biologic therapies, have been shown in RCT to improve HRQOL, including prasterone, mycophenolate mofetil, abetimus sodium, oral contraceptive, and hormone replacement therapy in the SELENA trials, as well as monoclonal antibodies epratuzumab and belimumab5,6,7,8. In 1998, an international consensus conference on outcome measures in rheumatology (OMERACT 4) recommended that 4 core domains be assessed in RCT and longitudinal observational studies (LOS) in SLE: disease activity, HRQOL, adverse events, and damage9. OMERACT has also recommended that both generic and disease-specific instruments be utilized to measure HRQOL. Ongoing efforts to develop promising therapies for SLE have demonstrated the importance of including patient-reported outcomes (PRO) to assess HRQOL in RCT. Measurement of HRQOL adds a unique dimension to … Address correspondence to Dr. Strand; E-mail: vstrand{at}stanford.edu
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".