Generation of evidence supporting the content validity of SF-36, FACIT-F, and LupusQoL, and novel patient-reported symptom items for use in patients with systemic lupus erythematosus (SLE) and SLE with lupus nephritis (LN)
Bibliographic record
Abstract
OBJECTIVE: SLE and lupus nephritis (LN) have significant impacts on the health-related quality of life of patients living with the condition, which are important to capture from the patient's perspective using patient-reported outcomes (PROs). The objectives of this study were to evaluate the content validity of PROs commonly used in SLE and LN (36-Item Short Form Health Survey (SF-36), Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) and Lupus Quality of Life (LupusQoL), as well as novel PRO symptom severity items measuring skin rash, joint pain, joint stiffness and swelling of the legs and/or feet, in both populations. METHODS: Qualitative, semi-structured, cognitive interviews were conducted with 48 participants (SLE=28, LN=20). Understanding and relevance of symptom and impact PRO concepts from existing PROs were assessed, alongside novel PRO symptom severity items with different recall periods (24 hours vs 7 days) and response scales (Numerical Rating Scale (NRS) vs Verbal Rating Scale). Interviews were conducted in multiple rounds to allow for modifications to the novel PRO items. Analysis of verbatim interview transcripts was performed. RESULTS: Symptom and impact concepts assessed by the SF-36, FACIT-F, and LupusQoL were well understood by both participants with SLE and LN (≥90.0%), with most considered relevant by over half of the participants asked (≥51.9%). All participants asked (100%) understood the novel PRO symptom severity items, and the majority (≥90.0%) considered the symptoms relevant. Minor modifications to the novel PRO items were made between rounds to improve clarity based on participant feedback. The selected 7-day recall period and NRS in the final iteration of the PRO items were understood and relevant. No differences in interview findings between the SLE and LN samples were identified. CONCLUSIONS: Findings provide evidence of content validity for concepts assessed by the SF-36, FACIT-F, LupusQoL and the novel PRO symptom severity items, supporting use of these PROs to comprehensively assess disease impact in future SLE and LN clinical trials.
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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 | 0.112 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".