Severe fatigue is associated with diminished lung function and elevated Galectin-9 levels in early systemic sclerosis
Bibliographic record
Abstract
Introduction: Symptoms resembling myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) frequently affect patients with rheumatic diseases, but little is known about their frequency and disease manifestations, particularly in systemic sclerosis (SSc) patients. We sought to determine if severe fatigue in SSc patients with early disease (< 7 years) is associated with increased disability, inflammation and fibrosis. Methods: In this exploratory cross-sectional study, 51 SSc patients were recruited locally (UofA cohort). Disability, disease damage accrual, inflammatory markers and, indicators of fibrotic and vascular complications (e.g. lung function, nailfold capillaroscopy) were compared between patients with and without severe fatigue. Fatigue was assessed using validated questionnaires (e.g. FACIT, MFI) and ME/CFS criteria. Findings were further corroborated in the national CSRG (Canadian Scleroderma Research Group) SSc cohort (n=126). Results: SSc patients with severe fatigue had significantly increased disability, reduced lung function capacity, and elevated Galectin-9 levels when compared to patients without fatigue. Galectin-9 levels correlated with reduced pulmonary function, and increased disease damage accrual. Further analysis in the UofA cohort suggested that indictors associated with disease progression such as reduced nailfold capillary density, and elevated VEGF, LTα and IL-16 were present in severely fatigued patients. Discussion: Severe fatigue in SSc patients is associated with increased disability, reduced pulmonary function and increased vascular remodeling. We propose that ME/CFS-like symptoms in patients with SSc may be indicative of sub-clinical inflammation and fibrosis. Further studies are required to determine whether Gal-9,may be a useful tool for the stratification of SSc patients - particularly those with severe fatigue resembling ME/CFS.
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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 | 0.000 | 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 | 0.000 | 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".