ArticleJournal of electrical bioimpedance2025
Predictive classification and regression models for bioimpedance vector analysis: Insights from a southern Cuban cohort.
Article in Journal of electrical bioimpedance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Metrology in Bioelectrical Impedance Analysis (BIA): From Measurement Science to Clinical and Research Applications.Sensors (Basel, Switzerland) · 2026Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study used predictive models to explore the link between bioparameters at characteristic frequency and their positions within tolerance ellipses in a southern Cuban cohort. The database includes 367 individuals (235 females, 132 males) aged 18-86. Among them, 61 had cancer, while 306 were healthy. After balancing the data, the analysis used 16 bioimpedance-based characteristics along with other anthropometric and location factors. The results showed that characteristic frequency bioparameters (Zc, θc, Xcc, and Rc) are key for assessing health and location. There was a strong agreement between experimental and predicted values for Zc, θc, Xcc, and Rc across various categories. Cancer patients showed higher Zc and slightly lower
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