Investigating the relationship between quantitative‐based ultrasound and MRI estimations of rotator cuff fatty infiltration
Bibliographic record
Abstract
INTRODUCTION: Fatty infiltration (FI) of the rotator cuff has important clinical implications. Quantitatively estimating FI using ultrasound (US) has considerable benefits for assessing FI in a non-invasive, accessible manner. This research investigated whether FI of the supraspinatus (SS) and infraspinatus (IS), estimated using US was related to intramuscular fat fractions measured from magnetic resonance images (MRI). METHODS: Data from 12 healthy young adult participants were used for analysis. US images of the SS and IS were captured using multiple transducer placement techniques from which echogenicity of the muscle region was quantified. Shoulder MRI were captured from which SS and IS were manually segmented and intramuscular fat fractions calculated. Six upper limb strength exertions were performed, resisted by a hand dynamometer. RESULTS: IS and SS echogenicity explained a significant amount of variance in MRI fat fractions for certain body positions and transducer techniques. Echogenicity agreement was higher for IS than SS. Significant relationships were identified between strength exertions and both echogenicity and MRI muscle volume, but not MRI fat fraction. CONCLUSIONS: This research provides preliminary evidence showing that quantitative-based US methods can be used to estimate MRI calculated fat fractions for the rotator cuff.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".