Diffusion tensor imaging in evaluation of human skeletal muscle injury
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: To explore the capability and reliability of diffusion tensor magnetic resonance imaging (DTI) in the evaluation of human skeletal muscle injury. MATERIALS AND METHODS: DTI of four patients with gastrocnemius and soleus muscles injuries was compared to eight healthy controls. Imaging was performed using a GE 3.0T short-bore scanner. A diffusion-weighted 2D spin echo echo-planar imaging (EPI) pulse sequence optimized for skeletal muscle was used. From a series of axially acquired diffusion tensor images the diffusion tensor eigenparameters (eigenvalues and eigenvectors), fractional anisotropy (FA), and apparent diffusion coefficient (ADC) were calculated and compared for injured and healthy calf muscles. Two dimensional (2D) projection maps of the principal eigenvectors were plotted to visualize the healthy and pathologic muscle fiber architectures. RESULTS: Clear differences in FA and ADC were observed in injured skeletal muscle, compared to healthy controls. Mean control FA was 0.23 +/- 0.02 for medial and lateral gastrocnemius (mg and lg) muscles, and 0.20 +/- 0.02 for soleus (sol) muscles. In all patients FA values were reduced compared to controls, to as low as 0.08 +/- 0.02. The ADC in controls ranged from 1.41 to 1.31 x 10(-9) m(2)/second, while in patients this was consistently higher. The 2D projection maps revealed muscle fiber disorder in injured calves, while in healthy controls the 2D projection maps show a well organized (ordered) fiber structure. CONCLUSION: DTI is a suitable method to assess human calf muscle injury.
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