ArticleRoyal Society open science2025
A causal model of human growth and its estimation using temporally sparse data.
Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
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Who cites it
3 citing papers in PubMed.
- Estimating Mean Growth Trajectories When Measurements Are Sparse and Age Is Uncertain.American journal of biological anthropology · 2026Article
- Pars basilaris size to estimate fetal and young infant age using forensic post mortem CT imaging.Forensic science, medicine, and pathology · 2025Article
- A causal model of human growth and its estimation using temporally sparse data.Royal Society open science · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Existing models of human growth provide limited insight into underlying mechanisms responsible for inter-individual and inter-population variation in children's growth trajectories. Building on general theories linking growth to metabolic rates, we develop a causal parametric model of height and weight growth incorporating a representation of human body allometry and a process-partitioned representation of ontogeny. This model permits separation of metabolic causes of growth variation, potentially influenced by nutrition and disease, from allometric factors, potentially under stronger genetic control. We estimate model parameters using a Bayesian multilevel statistical design applied to temporally dense height and weight measurements of U.S. children, and temporally sparse measurements of Indigenous Amazonian children. This facilitates a comparison of the contributions of metabolism and allometry to observed cross-cultural variation in the growth trajectories of the two populations, and permits simulation of the effects of healthcare interventions on growth. This theoretical model provides a new framework for exploring the causes of growth variation in our species, while potentially guiding the development of appropriate, and desired, healthcare interventions in societies confronting growth-related health challenges, such as malnutrition and stunting.
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Registered trials
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