<i>New Insights into Body Composition and Health:</i> Through Imaging Analysis: 2007 Ryley-Jeffs Memorial Lecture
Bibliographic record
Abstract
From calipers to magnetic resonance imaging (MRI), we have come a long way in our ability to analyze body composition. Some historical milestones are a reminder that many concepts in muscle and fat metabolism, and their measurement, have stood the test of time. However, newer imaging technology has improved our understanding of population heterogeneity in body composition, and the potential health problems associated with certain body composition phenotypes. Imaging analyses, such as dual energy X-ray absorptiometry, computed tomography, and MRI, have provided detailed characterization of the type and amount of fat deposited centrally (abdominal adipose tissue), the trajectory of losses in muscle tissue (sarcopenia), and the combination of low muscle mass/high fat mass (sarcopenic obesity). The last is a new emerging health concern because the presence of these two disproportionate tissue depots may have an additive effect on increasing morbidity. Ongoing measurement of body composition is needed, and preliminary research suggests this may have important nutritional implications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".