Measurement of skeletal muscle radiation attenuation and basis of its biological variation
Bibliographic record
Abstract
Skeletal muscle contains intramyocellular lipid droplets within the cytoplasm of myocytes as well as intermuscular adipocytes. These depots exhibit physiological and pathological variation which has been revealed with the advent of diagnostic imaging approaches: magnetic resonance (MR) imaging, MR spectroscopy and computed tomography (CT). CT uses computer-processed X-rays and is now being applied in muscle physiology research. The purpose of this review is to present CT methodologies and summarize factors that influence muscle radiation attenuation, a parameter which is inversely related to muscle fat content. Pre-defined radiation attenuation ranges are used to demarcate intermuscular adipose tissue [from -190 to -30 Hounsfield units (HU)] and muscle (-29 HU to +150 HU). Within the latter range, the mean muscle radiation attenuation [muscle (radio) density] is reported. Inconsistent criteria for the upper and lower HU cut-offs used to characterize muscle attenuation limit comparisons between investigations. This area of research would benefit from standardized criteria for reporting muscle attenuation. Available evidence suggests that muscle attenuation is plastic with physiological variation induced by the process of ageing, as well as by aerobic training, which probably reflects accumulation of lipids to fuel aerobic work. Pathological variation in muscle attenuation reflects excess fat deposition in the tissue and is observed in people with obesity, diabetes type II, myositis, osteoarthritis, spinal stenosis and cancer. A poor prognosis and different types of morbidity are predicted by the presence of reduced mean muscle attenuation values in patients with these conditions; however, the biological features of muscle with these characteristics require further investigation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".