Body composition and measurement options
Bibliographic record
Abstract
The most commonly used method for determining nutritional status to date is the BMI (Body Mass Index). From a scientific point of view, as it is based only on body weight and height, it can be misleading in many cases and is of limited use for the professional assessment of health status and obesity. Today there are more modern and accurate methods. Bioimpedance-based body composition assessment (BIA) systems provide detailed information on body weight, muscle and fat mass and water content. The frequency ranges and measurement points used have a major impact on the accuracy of the measurement data, and the Inbody series excels in this respect. Unlike other devices, it can also provide lean body mass and fat mass broken down by limb. Changes in nutritional status can be used as a predictor of disease progression, so the results of body composition measurement have applications in many areas of healthcare. Examples include diet therapy, obesity management, perioperative care, sports nutrition, supplementation of various therapies (oncology, nephrology, diabetology, cardiology, etc.), rehabilitation. Considering all these factors, it would be worthwhile to extend the use of BIA-based devices to the field of primary prevention (e.g. general practitioners, population screening, workplace health promotion).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.025 |
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".