Examination of the confounding effect of subcutaneous fat on muscle echo intensity utilizing exogenous fat
Bibliographic record
Abstract
We aimed to provide an unbiased estimate of the confounding effect of subcutaneous fat thickness on ultrasound echo intensity (EI) measures of muscle quality. The effect of fat thickness on EI was verified for an approximate range of 0 to 3 cm of fat using exogeneous layers of pork fat over the human tibialis anterior muscle. Sonograms were obtained (i) with focus constant across fat thickness conditions, and (ii) with focus position adjusted to the muscle region of interest (ROI) position for each fat thickness level. In agreement with our hypothesis, increasing fat between the probe and the ROI resulted in a decrease in EI. This overestimating effect of fat on muscle quality differs between sonograms with constant focus and sonograms with focus position adjusted to the vertical displacement in ROI position that occurs for different levels of fat thickness. Correcting equations to account for the overestimating effect of fat on muscle quality are provided for both focus conditions. This is the first study to systematically analyze the confounding effect of fat thickness as an independent factor and the provided equations can be used for improved accuracy in estimates of muscle quality in obese/overweight subjects/patients. Novelty: The independent confounding effect of subcutaneous fat thickness on ultrasound (US) estimates of muscle quality was quantified. US estimates of muscle quality depend on whether focus is adjusted to the muscle region of interest or not. Equations for correcting muscle quality estimates are provided.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".