Fatigue Measurements in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Fatigue is a frequent, disabling issue in systemic lupus erythematosus (SLE). It is, however, difficult to quantify. The Ad Hoc Committee on SLE Response Criteria for Fatigue in 2007 recommended using the Krupp Fatigue Severity Scale (FSS). Since then, the Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue Scale has also been validated in SLE. We performed a review of instruments used to measure fatigue in adult SLE patients from 2007 onward. METHODS: We searched PubMed, Medline, and Embase (January 2008-October 2017), identifying clinical trials and observational studies in adult SLE, where fatigue was a specifically measured outcome. All English and French studies were reviewed to determine fatigue measures and results. RESULTS: Thirty-seven studies met inclusion criteria. Eight scales were used. The visual analog scale (VAS), FSS, and FACIT-Fatigue Scale were most frequent. FSS was the most often used instrument in both clinical trials and observational studies. Twenty-five of the 37 studies demonstrated a difference in fatigue that was statistically significant and clinically meaningful. Of the 12 studies that did not, 6 used FSS, 3 used VAS, 2 used the Multidimensional Assessment of Fatigue, and 1 used the Brief Fatigue Index. All 6 studies using the FACIT-Fatigue Scale detected clinically meaningful and statistically significant differences. CONCLUSION: VAS, FSS, and FACIT-Fatigue Scale were the most frequently used instruments in adult SLE studies from 2008 to 2017. Many studies detected clinically important changes in fatigue. Fatigue remains a key measure in both clinical trials and observational SLE studies.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".