Advanced imaging techniques for assessment of undernutrition
Bibliographic record
Abstract
The use of new techniques has allowed the assessment of body composition to emerge as a fundamental part of nutritional assessment in clinical populations, as distinct body tissues are associated with specific health outcomes. This chapter focuses on the imaging methods: bioelectrical impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA) and, more recently, computed tomography (CT) and ultrasound (US) imaging which are appear to be the most convenient for use in clinical practice. Imaging methods for the assessment of body composition include DXA, CT, magnetic resonance imaging (MRI) and US, and three-dimensional (3D) (photonic) imaging. With the exception of US and 3D imaging, imaging techniques are now considered to be the most accurate tools for measuring adipose tissue and organs in clinical research. The use of these techniques has revolutionised the capacity to assess body composition and aid in the diagnosis and monitoring of a multitude of disease states and treatment regimes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".