Fatigue levels and associated factors in systemic sclerosis: a cross-sectional study of 2385 SPIN Cohort participants
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to compare fatigue in a large multinational SSc cohort with general population data and identify associated sociodemographic, lifestyle and SSc disease factors. METHODS: Scleroderma Patient-centered Intervention Network Cohort participants completed the Patient-Reported Outcomes Measurement Information System-29 v2.0 fatigue domain. T-scores were compared with the USA general population (mean = 50; s.d. = 10). Multivariable linear regression was used to assess associations with sociodemographic, lifestyle, and disease-related variables. RESULTS: Among the 2385 participants [mean age 54.9 (s.d. = 12.6) years, 87% female, 38% dcSSc], the mean fatigue T-score was 54.6 (s.d. = 11.0); 438 (18%) reported mild fatigue, 641 (27%) moderate fatigue, and 180 (8%) severe fatigue. Fatigue was independently associated with sociodemographic factors age [-0.10 points per year, (95% CI -0.14, -0.07)], male sex [-1.67 points, (-2.96, -0.37)], non-married status [0.97 points (0.04, 1.89)] and country [reference USA; France -2.35 points (-3.48, -1.21) and UK 2.38 points (0.80, 3.97)], and lifestyle factors smoking [4.16 points (2.52, 5.80)], alcohol consumption [-0.18 points per drink per week (-0.28, -0.07)] and BMI [0.34 points per unit (0.27, 0.42)]. Fatigue was associated with disease-related factors, including gastrointestinal involvement [4.21 points (2.99, 5.43)], digital ulcers [1.51 points, (0.25, 2.77)], moderate small joint contractures (1.41 points [0.13, 2.69]), RA [4.34 points (2.37, 6.31)] and SS [1.89 points (0.23, 3.55)]. When pain was included in the model, its association was large [2.19 points (2.03, 2.34)], and interstitial lung disease was also associated [1.21 points (0.42, 2.00)]. CONCLUSION: In people with SSc, fatigue scores were substantially higher than in the general population and associated with multiple disease factors, including gastrointestinal involvement, several painful disease manifestations, and lung involvement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".