The FATIGUE-PRO: a new patient-reported outcome instrument to quantify fatigue in patients affected by systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: This study aimed to implement a patient-centred and evidence-based approach to develop a novel patient-reported outcome (PRO) instrument to measure fatigue in patients with SLE. METHODS: A three-step mixed methods psychometric (MMP) approach was followed. Steps comprised first draft item generation and review using interview data; evaluation and refinement of second draft items using mixed methods data, including interview and quantitative data from a phase 2 clinical study in SLE analysed using Rasch Measurement Theory (RMT) analysis; and evaluation of the final FATIGUE-PRO items using RMT and complementary Classical Test Theory (CTT) analyses. Guided by MMP criteria, a team of clinicians and outcome-measurement experts assessed evidence to inform instrument development. RESULTS: Step 1 culminated in 55 items (n = 39 patients interviewed). Their refinement in step 2 using mixed methods evidence led to the final FATIGUE-PRO instrument comprising 31 items across three scales of fatigue: physical fatigue (9 items), mental and cognitive fatigue (11 items) and susceptibility to fatigue (11 items). Qualitative (n = 43 patients) and quantitative (n = 106 patients) evidence strongly supported the scales' content comprehensiveness and targeting, item quality and fit, conceptual uniqueness and appropriateness of the response scale. The FATIGUE-PRO further benefited from excellent reliability (RMT: 0.92-0.94 and CTT: 0.95-0.96) and supportive evidence of construct validity from assessments against other PROs. CONCLUSION: The conceptual advances, comprehensive coverage and strong psychometric properties of the FATIGUE-PRO will significantly advance the measurement and management of fatigue in SLE, both in clinical trials and routine practice. TRIAL REGISTRATION: ClinicalTrials.gov (https://clinicaltrials.gov), NCT02804763.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".