Diffusion Tensor Imaging in the Human Spinal Cord: Development, Limitations, and Clinical Applications
Bibliographic record
Abstract
Diffusion tensor imaging (DTI) is currently the only non-invasive in vivo assessment of white matter tract integrity. Capitalizing on the diffusion properties of water within an axon, DTI enables the visualization of tissue structure at a microscopic scale. Furthermore, measurements of anisotropy and diffusivity enable the detection of subtle details of the effects of injury that cannot be detected using conventional magnetic resonance techniques. Recently, DTI has been applied to the spinal cord, and results have demonstrated it to be a valuable tool for assessing the extent of white matter damage in numerous spinal cord-related conditions including multiple sclerosis, spinal cord injury, amyotrophic lateral sclerosis, myelitis, and spinal cord tumors. The purpose of this review is to discuss the technical limitations of the imaging method within the spinal cord, review possible solutions, and highlight the current uses and the potential clinical application of this technique.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".