Bioelectrical Impedance Vector Analysis for Nutritional Status Assessment in Systemic Sclerosis and Association With Disease Characteristics
Bibliographic record
Abstract
Objective To use bioelectrical impedance vector analysis (BIVA) in a cohort of patients with systemic sclerosis (SSc) in order to assess their nutritional status in comparison to other groups of patients and to find any correlation with clinical characteristics and outcome of the disease. Methods We retrospectively collected data from 50 SSc patients who underwent BIVA for clinical suspicion of malnutrition and compared them with patients affected by other chronic autoimmune rheumatic diseases (OCAD, n = 27) and those who were only symptomatic of malnutrition but without autoimmune features (n = 15), and with 50 healthy controls (HC). Results Patients with SSc presented significantly lower values of phase angle (PhA), basal metabolic rate (BMR), and body cellular mass (BCM), and an increase in extracellular water (ECW; P < 0.01 for all) than HC; instead, there were no significant differences for BMI. No significant differences were found between SSc and OCAD. Among patients with SSc, age directly correlated with ECW (ρ = 0.342, P = 0.015) and inversely with PhA (ρ = –0.366, P = 0.009). Female sex, anemia, hypoalbuminemia, reflux, and early satiety/abdominal distension associated with relevant alterations in BIVA results. BIVA variables were significantly different when cardiopulmonary and microvascular involvement was present. Four patients died during the study: they had significantly (P ≤ 0.01) lower PhA, BMR, and BCM, with an increased ECW. Conclusion BIVA, unlike BMI, allowed an accurate characterization of SSc patients at risk of malnutrition, correlating with serological malnutrition markers, with SSc-specific organ manifestations (cardiopulmonary involvement and microvascular damage), and with mortality. BIVA variables might represent a surrogate marker of damage accrual that leads to malnutrition, thus playing a leading role in the prognostic stratification of SSc patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".