Real‐time sonography to estimate muscle thickness: Comparison with MRI and CT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: We investigated the feasibility of using real-time sonography to measure muscle thickness. Clinically, this technique would be used to measure the thickness of human muscles in which intramuscular microstimulators have been implanted to treat or prevent disuse atrophy. METHODS: Porcine muscles were implanted with microstimulators and imaged with sonography, MRI, and CT to assess image artifacts created by the microstimulators and to design protocols for image alignment between methods. Sonography and MRI were then used to image the deltoid and supraspinatus muscles of 6 healthy human subjects. RESULTS: Microstimulators could be imaged with all 3 methods, producing only small imaging artifacts. Muscle-thickness measurements agreed well between methods, particularly when external markers were used to precisely align the imaging planes. The correlation coefficients for sonographic and MRI measurements were 0.96 for the supraspinatus and 0.97 for the deltoid muscle. Repeated sonographic measurements had a low coefficient of variation: 2.3% for the supraspinatus and 3.1% for the deltoid muscle. CONCLUSIONS: Real-time sonography is a relatively simple and inexpensive method of accurately measuring muscle thickness as long as the operator adheres to a strict imaging protocol and avoids excessive pressure with the transducer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it