Predictors for Nonaccidental Trauma in a Child With a Fracture—A National Inpatient Database Study
Bibliographic record
Abstract
BACKGROUND: Despite heightened awareness and multidisciplinary efforts, a predictive model to help the clinician quantify the likelihood of nonaccidental trauma (NAT) in a child presenting with a fracture does not exist. The purpose of this study was to develop an evidence-based likelihood of NAT in a child presenting with a fracture. METHODS: Using the 2012 Kids' Inpatient Database, we identified all available pediatric inpatients admitted with an extremity or spine fracture. Children with a fracture were subcategorized based on the diagnosis of NAT. Multivariate analysis using multiple logistic regression was used to generate odds ratios and create a predictive model for the probability of NAT in a child with a fracture. RESULTS: Of the 57,183 pediatric fracture cases, 881 (1.54%) had a concurrent diagnosis of NAT. Of these children, those presenting with multiple fractures had the highest rate of NAT (2.8%). The overall mortality rate in patients presenting with fractures and abuse was 1.8%, which was twice as high as patients without abuse (odds ratio [OR] = 2.0). Based on multivariate analysis, younger age (OR = 0.5), black race (OR = 1.7), intracranial injury (OR = 3.7), concomitant rib fracture (OR = 7.2), and burns (OR = 8.3) were positive predictors of NAT in a child with a fracture. A weighted equation using regression coefficients was generated and plotted on a receiver operative characteristic curve, demonstrating excellent correlation and probability of NAT (area under curve = 0.962). (Equation - ln (P/(1 - P)) = -1.79 - 0.65 (age in years) + 0.51 (black race) + 1.97 (rib fracture) + 1.31 (intracranial injury) + 2.12 (burn)). CONCLUSION: Using a large, national inpatient database, we identified an overall prevalence of 1.54% of NAT in children admitted to the hospital with a fracture. Based on five independent predictors of NAT, we generated an estimated probability chart that can be used in the clinical workup of a child with a fracture and possible NAT. This evidence-based algorithm needs to be validated in clinical practice. LEVEL OF EVIDENCE: Prognostic study, Level III (case-control study).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".