Management of perinatal cervical spine injury using custom-fabricated external orthoses: design considerations, narrative literature review, and experience from the Hospital for Sick Children. Illustrative cases
Bibliographic record
Abstract
BACKGROUND: Cervical spine injuries in neonates are rare and no guidelines are available to inform management. The most common etiology of neonatal cervical injury is birth-related trauma. Management strategies that are routine in older children and adults are not feasible due to the unique anatomy of neonates. OBSERVATIONS: Here, the authors present 3 cases of neonatal cervical spinal injury due to confirmed or suspected birth trauma, 2 of whom presented immediately after birth, while the other was diagnosed at 7 weeks of age. One child presented with neurological deficits due to spinal cord injury, while another had an underlying predisposition to bony injury, infantile malignant osteopetrosis. The children were treated with a custom-designed and manufactured full-body external orthoses with good clinical and radiographic outcomes. A narrative literature review further supplements this case series and highlights risk factors and the spectrum of birth-related spinal injuries reported to date. LESSONS: The current report highlights the importance of recognizing the rare occurrence of cervical spinal injury in newborns and provides pragmatic recommendations for management of these injuries. Custom orthoses provide an alternate option for neonates who cannot be fitted in halo vests and who would outgrow traditional casts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".