Quantitative MRI in a non‐surgical model of cervical spinal cord injury
Bibliographic record
Abstract
Quantitative T2 (qT2), diffusion tensor imaging (DTI), and histology were used to investigate a cervical model of spinal cord injury (SCI) in the rat. While quantitative MRI can significantly increase the specificity in the presence of pathology, it must be validated for each type of injury or disease. In the case of traumatic SCI most models are difficult to image, either due to the location of the injury, or as a result of damage to surrounding tissues resulting from invasive surgical procedures. In this study a non-surgical cervical model of SCI, produced using a combination of focused ultrasound and microbubbles, was used to produce pathology similar to that seen in models of contusive and compressive injuries. qT2 and DTI were performed at 24 h and 1 and 2 weeks following injury, and compared with H&E and luxol fast blue histology. In the injured spinal cord, in addition to intra/extracellular (I/E) water and myelin water in white matter, qT2 revealed a large component with very short T2 of about 3 ms, which was highly correlated with the presence of hemorrhage in both gray and white matter at 24 h, and with the presence of hemosiderin in gray matter at 2 weeks following injury. The T2 of the I/E water peak was also elevated at 24 h in both gray and white matter, which was correlated with the presence of vacuolation/edema on histology. Cystic cavities were only seen at the 1 or 2 week timepoints, and were correlated with the presence of a water peak with T2 > 250 ms. No significant changes in diffusivity parameters were observed. Pathologies were often co-occurring, with opposite effects on the average T2 in a given voxel, reducing the visibility of injured tissue on standard T2 -weighted MR images.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".