Is Serum Albumin a Marker of Malnutrition in Chronic Disease? The Scleroderma Paradigm
Bibliographic record
Abstract
BACKGROUND: Malnutrition is common in many chronic diseases, but physicians may rely on a low albumin value before deciding that malnutrition is present. OBJECTIVE: To determine the relationship between serum albumin and malnutrition in systemic sclerosis (SSc) as a paradigm for other chronic diseases. DESIGN: Cross-sectional, multicenter study of patients from the Canadian Scleroderma Research Group Registry. We used the Malnutrition Universal Screening Tool (MUST) to evaluate patients for malnutrition. Disease extent was measured in several ways, including physician global assessment. Multiple linear regression was performed to identify independent predictors of serum albumin. RESULTS: Two hundred fifty-eight patients were studied. The mean (SD) serum albumin level was 44.4 (4.2) g/L. Only 2% of the values were below normal and all these patients were in MUST category > or =2, or high risk for malnutrition, which included 21.3% of the cohort. MUST, shorter disease duration, greater disease severity (physician global assessment of disease severity and modified Rodnan skin score), and greater disease activity (physician global assessment of disease activity, C-reactive protein, and Scleroderma Disease Activity Index) all correlated significantly but weakly with albumin. Multivariate analysis demonstrated that a higher MUST score and worse disease severity were independently associated with lower serum albumin, but only 7% of the variance of albumin was explained in the adjusted model. CONCLUSIONS: Serum albumin is not useful as a marker for malnutrition in SSc and should not be assumed to be useful as a marker in other chronic diseases. More attention should be paid to clinical features of malnutrition, including assessment of body mass index and unplanned weight loss, and overall disease severity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".