Impaired Health Status and the Effect of Pain and Fatigue on Functioning in Clinical Trial Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Our study evaluated the impaired health status of clinical trial patients with systemic lupus erythematosus (SLE) and explored the relationship between changes in fatigue and pain and their effect on overall health status. METHODS: Pooled treatment and placebo data from a phase Ib clinical trial of adults with moderate/severe SLE were analyzed. Measures included patient-reported Medical Outcome Study Short Form-36 Survey, Version 2 (SF-36v2), Fatigue Severity Scale, and numeric rating scales (NRS) for pain and global health assessment and clinician-reported global assessment of disease activity (MDGA). Disease burden was compared to the US general population. Health status of responders and nonresponders on pain or fatigue were compared. RESULTS: The sample included 161 patients with SLE, predominantly female (96%) and white (72%), with average age of 43 ± 11 years. Mean SF-36v2 component summary scores reflected overall problems with physical [physical component summary (PCS); 35.2 ± 9.7] and mental health (mental component summary; 40.9 ± 12.9). Patients with SLE had worse health status on all SF-36v2 subscales than the US general population and comparable age and sex norms (effect size -0.51 to -2.15). Pain and fatigue responders had greater improvements on SF-36v2 scores (bodily pain, physical functioning, social functioning, PCS), patient global health assessment NRS, and MDGA than nonresponders. There was moderate agreement in responder status, based on global assessments by patients and clinicians (68.1%), with some discrepancy between patients who were MDGA responders but patient assessment nonresponders (27.7%). CONCLUSION: Improvements in patient-reported pain or fatigue correlated with improvements in overall health. Patient assessments offer a unique perspective on treatment outcomes. Patient-reported outcomes add value in understanding clinical trial treatment benefits.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Analysis of health status, pain and fatigue among lupus clinical trial patients; uses trial data to answer a clinical question.
The study examines health outcomes and treatment responses in patients with systemic lupus erythematosus.
Clinical analysis of pain/fatigue PROs in SLE trial patients; domain outcomes, not study of research methods.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".