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Record W3092882607 · doi:10.3899/jrheum.200964

Bioelectrical Impedance Vector Analysis for Nutritional Status Assessment in Systemic Sclerosis and Association With Disease Characteristics

2020· article· en· W3092882607 on OpenAlexvenueno aff
Marco Di Battista, Simone Barsotti, Alessia Monaco, Alessandra Rossi, Alessandra Della Rossa, Marta Mosca

Bibliographic record

VenueThe Journal of Rheumatology · 2020
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBioelectrical impedance analysisHypoalbuminemiaInternal medicineWastingMalnutritionGastroenterologyCardiologyScleroderma (fungus)Body mass indexImmunology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.264
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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