Radiological Diagnosis of Suspected Scaphoid Fractures in Children: A Systematic Review
Bibliographic record
Abstract
Background: Children presenting with suspicion of a scaphoid fracture pose a diagnostic challenge. Several imaging modalities such as plain radiograph (XR), computed tomography (CT), and magnetic resonance imaging (MRI) have been previously described. Timely and accurate diagnosis is important to avoid overtreatment, and complications, and allow for an earlier return to activity. It is unclear which imaging modality is the most diagnostically accurate for detecting scaphoid fractures in this population. Methods: A systematic review was conducted in concordance with established guidelines to elucidate the diagnostic accuracy of various imaging modalities for detecting scaphoid fractures in children. A comprehensive literature search of electronic databases was developed by experienced librarians. All steps were performed independently by 2 reviewers. Results: Eight articles were included, all evaluating plain radiographs as the index test. One study evaluated CT. XR demonstrated sensitivity values ranging from 16% to 54%, with specificity of 71% to 100%. CT had 95% sensitivity with MRI as the reference standard. The included studies were limited by poor methodologic quality and heterogeneous patient populations. Conclusions: XR demonstrates a wide range of diagnostic accuracy in diagnosing scaphoid fractures in children. CT and MRI, while promising, are limited by a lack of evidence in children. More pediatric-specific prospective studies are required to guide the choice of diagnostic imaging in children with suspected scaphoid 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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".