Measuring fatigue in SSc: a comparison of the Short Form-36 Vitality subscale and Functional Assessment of Chronic Illness Therapy–Fatigue scale
Bibliographic record
Abstract
OBJECTIVE: Fatigue is a common and important problem in SSc. No studies, however, have compared the properties of fatigue measures in SSc. The objective of this study was to compare the performances of the Short Form-36 (SF-36) Vitality subscale and Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT) in SSc. METHODS: Cross-sectional, multi-centre study of Canadian Scleroderma Research Group Registry patients. The associations of the two instruments with other patient-reported outcome measures, as well as physician- and patient-rated disease variables were compared. Item response theory models were used to compare the degree to which items and the total scores of each measure effectively covered the full spectrum of fatigue levels. RESULTS: There were 348 patients (297 women, 85%) in the study. The instruments correlated at r = 0.65 with each other. The FACIT tended to correlate slightly higher than the SF-36 Vitality subscale with physician- and patient-rated disease variables and patient-reported physical function and disability, whereas the SF-36 Vitality subscale correlated minimally higher with mental health measures. The FACIT had markedly better discrimination across the range of fatigue, particularly at average to high fatigue levels, whereas the SF-36 Vitality subscale discriminated well only among patients in the low to average range. CONCLUSION: The FACIT discriminates better than the SF-36 Vitality subscale at average to high ranges of fatigue, which is common in SSc, suggesting that it is preferred for measuring fatigue in SSc.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 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".