The Role of Magnetic Resonance Imaging in the Management of Acute Spinal Cord Injury
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) has become the gold standard for imaging neurological tissues including the spinal cord. The use of MRI for imaging in the acute management of patients with spinal cord injury has increased significantly. This paper used a vigorous literature review with Downs and Black scoring, followed by a Delphi vote on the main conclusions. MRI is strongly recommended for the prognostication of acute spinal cord injury. The sagittal T2 sequence was particularly found to be of value. Four prognostication patterns were found to be predictive of neurological outcome (normal, single-level edema, multi-level edema, and mixed hemorrhage and edema). It is recommended that MRI be used to direct clinical decision making. MRI has a role in clearance, the ruling out of injury, of the cervical spine in the obtunded patient only if there is abnormality of the neurological exam. Patients with cervical spinal cord injuries have an increased risk of vertebral artery injuries but the literature does not allow for recommendation of magnetic resonance angiography as part of the routine protocol. Finally, time repetition (TR) and time echo (TE) values used to evaluate patients with acute spinal cord injury vary significantly. All publications with MRI should specify the TR and TE values used.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".