Editorial Comment: Advancing Muscle Imaging With Quantitative Approaches and Emerging Medical Concepts
Bibliographic record
Abstract
The transformation of musculoskeletal imaging from its traditional subjective and qualitative evaluation of imaging examinations to a more objective and quantitative analysis of imaging data is anticipated to significantly enhance the impact of radiologists' interpretations in diagnosing diseases, predicting outcomes, and monitoring treatments.Although muscle imaging has taken a back seat compared with bone and joint imaging, this distinction is set to change with innovative quantitative techniques.The importance of the body's 650 skeletal muscles for maintaining autonomy and quality of life cannot be overstated.The aging process leads to a noticeable decline in skeletal muscle mass, typically beginning around one's late fifties [1] and compounded by various pathologic factors.This article sheds light on less familiar clinical conditions-sarcopenia, frailty, and cachexia-and equips radiologists with a deeper understanding of those conditions.Readers will also gain insights into the relationship between quantitative imaging of muscle steatosis and fibrosis and the outcomes of these disorders.The article delves into quantifying muscle atrophy, steatosis, and fibrosis using CT, MRI, and ultrasound, introducing readers to current clinically available imaging techniques.Furthermore, the authors touch on emerging medical concepts such as geroscience and biologic age, offering readers a forward-looking perspective on health care.Recent advances, including the opportunistic use of CT [2] and MRI scans for tissue composition metrics and automated image segmentation and
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.045 | 0.037 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".