Cervical spinal cord injection of epidural corticosteroids: Comprehensive longitudinal study including multiparametric magnetic resonance imaging
Bibliographic record
Abstract
Despite widespread use, the efficacy of epidural corticosteroid injections (ESI) for osteoarthritis-associated neck or radicular pain remains uncertain, so even rare serious complications enter into discussions about use. However, various factors impede investigation and publication of serious adverse events. To that end, we developed new magnetic resonance imaging (MRI) techniques for spinal cord white matter quantification and used the best available physiological tests to characterize a cervical spinal cord lesion caused by inadvertent intramedullary injection of Depo-Medrol. A 29-year-old woman with mild cervical osteoarthritis had 2 years of headache and neck pain (concussion and whiplash) after 2 minor motor vehicle accidents. During C5-6 ESI, she developed new left-sided motor and sensory symptoms, and MRI demonstrated a new left dorsal spinal cord cavity. Mild left-sided motor and sensory symptoms have persisted for more than 2.5 years, during which time we performed serial neurological examinations, standard electrodiagnostics, somatosensory evoked potentials, and transcranial measurement of corticospinal central motor conduction time (CMCT). We used 3-Tesla MRI with a 32-channel coil developed for high-resolution cervical spinal cord structural imaging, diffusion tensor imaging (DTI), and magnetization transfer (MT). T(2)(∗)-weighted signal and DTI and MT metrics showed delayed spread of the lesion across 4 vertebral levels rostrally, consistent with Wallerian degeneration within the ascending left dorsal columns. However, only CMCT metrics detected objective correlates of her left hemiparesis and bilateral hyperreflexia. DTI and MT metrics may better distinguish between post-traumatic demyelination and axonal degeneration than conventional MRI. These tests should be considered to better characterize similar spinal cord injuries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".