The epidemiology and clinical features of multiple, non-contiguous spine injuries in children
Bibliographic record
Abstract
Multiple – non-contiguous spinal injuries (NCSI) are complex injuries frequently missed and with increased potential for adverse outcomes. Adult incidence is reported as 1.6–34%, with greater risk of mortality and fracture instability; the paediatric incidence is unknown. A retrospective review at an academic paediatric trauma centre over a 15 year period identified 25 (11.8%) out of 211 patients with NCSI, with a mean age of 10.7 years. MVC was the primary cause for ages 0–9, while a fall was seen for ages 10–17. The mean number of vertebral levels injured was 3.2. The most common region was the thoracic spine, with a mean of 5.4 (range 1–22) intact vertebral levels between injuries. Twenty-four percent with NCSI had a neurologic deficit versus 9.7% with single level, contiguous injuries (RR 2.48 (1.09, 5.65)). Seven (78%) of nine patients aged 0–9 suffered an associated injury, usually a visceral injury. Mortality was 8.0% in patients with NCSI versus 2.7% in patients with single level or contiguous injury. Children with NCSI have a higher mortality and suffer more serious neurologic injury than with single level injury. Injured spinal segments occur at a greater distance in patients with neurologic injury or death. Younger patients with NCSIs had a high rate of associated injuries. Clinicians must be aware of the incidence of NCSIs in children, as well as their associations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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".