Pediatric Traumatic Spinal Cord Injury in the United States: A National Inpatient Analysis
Bibliographic record
Abstract
BACKGROUND: Traumatic spinal cord injury (tSCI) is a debilitating neurological condition often associated with lifelong disability. Despite this, there are limited data on pediatric tSCI epidemiology in the United States. OBJECTIVES: Our primary objective was to estimate tSCI hospitalization rates among children, including by age, sex, and race. Secondary objectives were to characterize tSCI hospitalizations and examine associations between sociodemographic characteristics and tSCI etiology. METHODS: We used the 2016 Kids' Inpatient Database to examine tSCI hospitalizations among children (<21 years). Descriptive statistics were used to report individual and care setting characteristics for initial tSCI hospitalizations. We used Census Bureau data to estimate tSCI hospitalization rates (number of pediatric tSCI hospitalizations / number of US children) and logistic regression modeling to assess associations between documented sociodemographic characteristics and injury etiology. RESULTS: There were 1.48 tSCI admissions per 100,000 children; highest rates of hospitalization involved older (15-20 years), male, and Black children. Hospitalization involving male (adjusted odds ratio [AOR] 0.43; 95% CI, 0.33-0.58) or Black (AOR 0.37; 95% CI, 0.25-0.55) children were less likely to involve a motor traffic accident. Hospitalizations of Black children were significantly more likely to have a diagnosis of tSCI resulting from a firearm incident (AOR 18.97; 95% CI, 11.50-31.28) or assault (AOR 11.76; 95% CI, 6.75-20.50) compared with hospitalizations of White children. CONCLUSION: Older, male, and Black children are disproportionately burdened by tSCI. Implementation of broad health policies over time may be most effective in reducing pediatric tSCI hospitalizations and preventable 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".