Spinal Cord Injuries without Radiographic Abnormality at Two Pediatric Trauma Centers in Ontario
Bibliographic record
Abstract
BACKGROUND: Spinal cord injury without radiographic abnormality (SCIWORA) represents a traumatic myelopathy, either transient or permanent, that is not associated with visible vertebral fractures or ligamentous abnormalities on plain radiographs or CT. MRI has become essential in the diagnosis and evaluation of trauma patients and in predicting the long-term neurological outcome. METHODS: The medical charts of 578 children with vertebral trauma at the Children's Hospital of Eastern Ontario and the Hospital for Sick Children were retrospectively reviewed. RESULTS: Forty-five patients were identified as having traumatic myelopathy. Three of these patients had SCIWORA. Two sustained thoracic spinal cord injuries as a result of motor vehicle accidents and permanent neurological deficits. The third was involved in a sports-related injury and sustained a cervical spine injury that improved in 48 h. CONCLUSIONS: SCIWORA is uncommon. The serious neurological sequelae that can result from a missed diagnosis merit more attention in identifying a spinal lesion in children with traumatic myelopathy. MRI has played a valuable role in this respect and may be even more predictive of outcome than the presenting neurological findings. The SCIWORA acronym can be modified to SCIWONA (spinal cord injury without neuroimaging abnormality) in order to highlight the importance of MRI in the prognosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".