Malnutrition Is Common in Systemic Sclerosis: Results from the Canadian Scleroderma Research Group Database
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a multisystem disease associated with significant morbidity and increased mortality. Little is known about nutritional status in SSc. We investigated the prevalence and demographic and clinical correlates of nutritional status in a large cohort of patients with SSc. METHODS: This was a cross-sectional multicenter study of patients (n = 586) from the Canadian Scleroderma Research Group Registry. Patients were assessed with detailed clinical histories, medical examinations, and self-administered questionnaires. The primary outcome was risk for malnutrition using the "malnutrition universal screening tool" (MUST). Multiple logistic regression was used to assess the relationship between selected demographic and clinical variables and MUST categories. RESULTS: Of the 586 patients in the study, MUST scores revealed that almost 18% were at high risk for malnutrition. The significant correlates of high malnutrition risk included the number of gastrointestinal (GI) complaints, disease duration, diffuse disease, physician global assessment of disease severity, hemoglobin, oral aperture, abdominal distension on physical examination, and physician-assessed possible malabsorption. Among 14 GI symptoms, only poor appetite and lack of a history of abdominal swelling and bloating predict MUST. These factors accounted for 24% of the variance in MUST scores. CONCLUSION: The risk for malnutrition in SSc is moderate and is associated with shorter disease duration, markers of GI involvement, and disease severity. Patients with SSc should be screened for malnutrition, and potential underlying causes assessed and treated when possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".