Dynamic and Functional Imaging of the Musculoskeletal System
Bibliographic record
Abstract
This afternoon I watched Humboldt, our yellow Labrador retriever, chase a squirrel across the back yard. His motion was fluid and graceful, as was that of his quarry (who escaped). The essence of the musculoskeletal system is this kind of coordinated motion. The fixed snapshots of bones and muscles provided to us by static imaging techniques only capture a small portion of what makes us move. Dynamic imaging of the musculoskeletal system allows us to observe the joints while in motion and under load. Kinematic, cine phase-contrast, and real-time magnetic resonance imaging (MRI) provides us the means to measure forces and compute velocities. We can examine parts of the musculoskeletal system, such as the spine while weight bearing, which may give us more specificity in the diagnosis of back pain. MRI has advanced to include techniques such as blood-oxygenation-level-dependent and T 2 -mapping that have the potential to provide us functional information about the coordination of the nervous system with the musculoskeletal system. This could be important in diagnosis or treatment of neuromuscular diseases such as cerebral palsy and stroke. These techniques also have the potential to reveal the basic mechanisms of action of nerves, muscles, and joints. Imaging of molecular processes in the musculoskeletal system has the potential to show levels of function not possible with conventional imaging. Positron emission tomography, high-field MRI, and other advanced techniques may allow in vivo assessment of perfusion, oxygenation, and metabolism. New molecular contrast agents using these techniques may provide better understanding of the pathogenesis and progression of joint disease. Routine, static imaging of the musculoskeletal system has provided us with much information about joint function. Patients that have a normal static imaging study may benefit from more advanced techniques. Dynamic and functional imaging methods provide the means for more accurate diagnosis and complete understanding of the musculoskeletal system. I would like to thank all of the authors contributing to this issue for their great cooperation and the timely submission of their articles. I would also like to thank our Editors in Chief, David Karasick and Mark Schweitzer, for asking me to be guest editor on this fascinating topic. Finally, I would like to thank Erik Wenskus, Thieme Production Editor, for his help in putting this exciting issue together.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".