Test-Retest Reliability of Muscle Thickness, Echo-Intensity and Cross Sectional Area of Quadriceps and Hamstrings Muscle Groups Using B-mode Ultrasound
Bibliographic record
Abstract
Ultrasound muscle images have been extensively used as tools for investigating, diagnosing and monitoring thigh muscles. However, there is a lack of information examining ultrasound reliability of quadriceps and hamstrings images for research and clinical use. Objectives: To determine the reliability of muscle thickness (MT), echo intensity (EI) and cross sectional area (CSA) of quadriceps and hamstrings muscle groups. Methods: Single transverse images of the rectus femoris (RF), vastus intermedius (VI), vastus medialis (VM), vastus lateralis (VL), biceps femoris long head (BFlh), semitendinosus (ST), and semimembranosus (SM) muscles were scanned in the right and left legs of ten healthy collegiate men (age 23.4 ± 2.2 yrs, mass 71.7 ± 11.7 kg, height 1.73 ± 0.06 m) between two sessions with one day interval. Intraclass correlation coefficients (ICCs), standard error of measurement (SEM), and minimum difference to be considered “real” (MD) were measured for MT, EI, and CSA. Results: A range of 0.97-0.99, 0.83-0.88, and 0.86-0.97 (ICC); 0.72-1.38, 2.73-3.41, and 0.36-1.04 (SEM); and 2.01-3.82, 7.56-9.46, and 0.99-2.89 (MD) were found for quadriceps muscles, and 0.93-0.99, 0.74-0.90, and 0.89-0.96 (ICC); 0.73-1.94, 3.29-4.98, and 0.69-1.08 (SEM); and 2.03-5.38, 9.13-13.81, and 1.91-2.98 (MD) were found for hamstrings muscles. Conclusions: These results suggest that ultrasound imaging of both quadriceps and hamstrings muscle architecture is a reliable technique for assessing thigh musculoskeletal tissue. The anatomical sites, as well as ultrasound adjustments, images, and results utilized here may assist future researchers and clinicians as reference tools when measuring quadriceps and hamstrings musculature.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".