Predictors of True Scaphoid Fractures in Children
Bibliographic record
Abstract
Background: The scaphoid is the most commonly fractured carpal bone in children. True scaphoid fractures have proven to be difficult to diagnose, as they may not be apparent on initial imaging. Children with clinical suspicion of a scaphoid fracture may be treated with continued immobilization, even in the absence of radiographic evidence of a fracture. The purpose of this study is to identify predictors of true scaphoid fractures in children to help guide management. Methods: This study is a retrospective cohort study of children presenting to a tertiary pediatric hospital with hand or wrist injuries. Patients were grouped based on the presence of a true scaphoid fractures (confirmed on imaging) or those with clinical suspicion of a scaphoid fracture alone (no radiographic evidence of fracture). Demographic and clinical characteristics were compared with univariate and multivariate statistics to identify fracture predictors. Results: One hundred and thirty patients were included in the study: 57 in the true scaphoid fracture group and 73 in the clinical scaphoid fracture group. Patients with a true scaphoid fracture were older than those with a clinical scaphoid fracture (median age [interquartile range], 14.2 [13.0-15.4] vs 12.9 [11.9-14.4], P = .01). Men were more likely to sustain a true scaphoid fracture (65.0% vs 35.0%, P = .01). Older age and male sex were shown to be independent predictors of true scaphoid fractures (odds ratio [95% confidence interval], 1.25 [1.03-1.50] and 2.93 [1.39-6.17], respectively). Conclusions: In the pediatric population, older age and male children may be at increased risk of true scaphoid fractures. This may help guide decisions surrounding further imaging and treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".