The Use of Technology for Estimating Body Composition
Bibliographic record
Abstract
Assessment of body composition, both at single time points and longitudinally, is particularly important in clinical nutrition practice. It provides a means for the clinician to characterize nutrition status at a single time point, aiding in the identification and diagnosis of malnutrition, and to monitor changes over time by providing real-time information on the adequacy of nutrition interventions. Objective body composition measurement tools are available clinically but are often underused in nutrition care, particularly in the United States. This is, in part, due to a number of factors concerning their use in a clinical context: cost and accessibility of equipment, as well as interpretability of the results. This article focuses on the factors influencing interpretation of results in a clinical setting. Body composition assessment, regardless of the method, is inherently limited by its indirect nature. Therefore, an understanding of the strengths and limitations of any method is essential for meaningful interpretation of its results. This review provides an overview of body composition technologies available clinically (computed tomography, dual-energy x-ray absorptiometry, bioimpedance, ultrasound) and discusses the strengths and limitations of each device.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".