Conversion of Functional Assessment of Chronic Illness Therapy–Fatigue to Patient‐Reported Outcomes Measurement Information System Fatigue Scores in Two Phase III Baricitinib Rheumatoid Arthritis Trials
Bibliographic record
Abstract
OBJECTIVE: The Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) is validated for measuring fatigue in rheumatoid arthritis (RA). However, 10 of 13 FACIT-F items are identified as relevant to patients with RA. The Patient-Reported Outcomes Measurement Information System (PROMIS) uses an item response theory-calibrated T score metric. The PROMIS Fatigue item bank includes the FACIT-F items, enabling score conversion. The performance of converted PROMIS Fatigue scores has not been evaluated in RA populations or clinical trials. Our objective was to assess the performance of converted PROMIS Fatigue scores in 2 RA clinical trials of baricitinib. METHODS: Crosswalk tables and pattern-scoring methods converted FACIT-F scores to PROMIS Fatigue for both the 13-item FACIT-F and the 10-item RA-optimized FACIT-F instrument, in 2 RA clinical trials evaluating baricitinib, RA-BEAM, and RA-BEACON. RA-BEAM patients had an inadequate response to methotrexate. RA-BEACON patients had an inadequate response or intolerance to ≥1 tumor necrosis factor inhibitor. Baricitinib was compared to all treatment arms via analysis of covariance on PROMIS Fatigue score conversions. RESULTS: Baseline FACIT-F-derived PROMIS Fatigue scores reflected severe fatigue across treatment groups and were similar using different scoring methods. At week 24 in both studies, baricitinib was associated with clinically meaningful improvements in PROMIS Fatigue scores. PROMIS Fatigue scores were consistent for conversion methods and for the 13-item or 10-item FACIT-F. CONCLUSION: All 4 conversion methods showed differentiation of active treatment compared with placebo from week 12, supporting the use of the PROMIS Fatigue and converting the 10-item FACIT-F to assess fatigue and demonstrate treatment benefit in RA clinical trials on a standardized metric.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".