Conventional MRI as a diagnostic and prognostic tool in spinal cord injury: a systemic review of its application to date and an overview on emerging MRI methods
Bibliographic record
Abstract
INTRODUCTION: The diagnosis and prognosis of traumatic spinal cord injury has historically relied on clinical examination whereby those presenting with severe injuries were deemed unlikely to recover and those presenting with mild injuries were deemed more likely to recover. With the widespread use of MRI to visualize traumatic injury to the spinal cord, a spectrum of previously unseen characteristics ranging from mild T2-weighted signal intensity to complete spinal cord transection is now available to aid in both the diagnosis and prognosis. AREAS COVERED: In this systematic review, the authors outline how clinical examination (using the American Spinal Injury Association standards) and MRI characteristics can be used to classify and characterize acute traumatic cervical spinal cord injury. The reader will gain an appreciation for the different magnetic resonance signal characteristics that can be used to predict a favorable or unfavorable prognosis following traumatic spinal cord injury. The accuracy of this information, in terms of sensitivity and specificity, is presented. Using likelihood ratios, the authors work through specific examples. EXPERT OPINION: The use of MRI in the evaluation of the human spinal cord has aided our understanding of the condition significantly. However, there are still several challenges that need to be met, in particular the use of MRI to detect functional abnormalities as well as structural ones. In the coming years, our ability to define damaged circuits in the spinal cord will mean that it will be possible to link structure to function in an objective non-invasive way, which will have implications for the understanding and potential treatment of spinal cord injury.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| 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) | 0.003 | 0.001 |
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".