Derivation and Internal Validation of a Prediction Model for Pediatric Hand Fracture Triage
Bibliographic record
Abstract
Background: Pediatric hand fractures are common, and most can be managed by a period of immobilization. However, it remains challenging to identify those more complex fractures requiring the expertise of a hand surgeon to ensure a good outcome. The purpose of this study was to develop a prediction model for identification of complex pediatric hand fractures requiring care by a hand surgeon. Methods: A 2-year retrospective cohort study of consecutively referred pediatric (<18 years) hand fracture patients was used to derive and internally validate a prediction model for identification of complex fractures requiring the expertise of a hand surgeon. These complex fractures were defined as those that required surgery, closed reduction, or four or more appointments with a hand surgeon. The model, derived by multivariable logistic regression analysis, was internally validated using bootstrapping and then translated into a risk index. Results: Of 1170 fractures, 416 (35.6%) met criteria for a complex fracture. Multivariable regression analysis identified six significant predictors of complex fracture: open fracture, rotational deformity, angulation, condylar involvement, dislocation or subluxation, and displacement. Internal validation demonstrated good performance of the model (C-statistic = 0.88, calibration curve p = 0.935). A threshold of ≥1 point (ie, any one of the predictors) resulted in a simple, easy-to-use tool with 96.4% sensitivity and 45.5% specificity. Conclusions: A high-performing and clinically useful decision support tool was developed for emergency and urgent care physicians providing initial assessment for children with hand fractures. This tool will provide the basis for development of a clinical care pathway for pediatric hand fractures.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".