Methodological standards for body composition—an expert-endorsed guide for research and clinical applications: levels, models, and terminology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Body composition assessment is widely used in both research and clinical practice, yet confusion over basic concepts and terminology persists, leading to inaccurate assessments, comparisons, and interpretations. To address this concern, an international working group was formed to clarify basic concepts, standardize terminology, and provide guidance on the use and interpretation of body composition assessment. This initial publication addresses methodological standards, focusing on summarizing body composition levels and models, and introducing standardized terms and definitions. Body composition is organized into 5 distinct levels, ranging from atomic to whole-body, with each higher level encompassing the components of the preceding less complex levels. As a result, terms that describe components at different levels should not be used interchangeably. For example, the use of the molecular-level term "lean body mass" is discouraged because it inaccurately refers to fat-free mass (FFM), lean mass, or lean soft tissue (LST). FFM includes all compartments at the molecular level except fat (nonpolar lipids; mainly triglycerides), and FFM also contains nonfat (or polar) lipids. The term "lean mass" is equivalent to FFM, but not to LST, as FFM includes bone mineral content. Additionally, skeletal muscle is classified at the tissue-organ level and should not be confused with the molecular-level components FFM and LST. Likewise, fat mass and adipose tissue are different components: fat mass, mainly triglycerides, is assessed at the molecular level, whereas adipose tissue is measured at the tissue-organ level. Models are also specific to each level. It is crucial for researchers and clinicians to have a clear understanding of what each body component entails and to use accurate terminology to ensure precise assessment, reporting, and interpretation of body composition data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it