Accuracy of Point-of-Care Ultrasonography for Pediatric Ankle Sprain Injuries
Bibliographic record
Abstract
OBJECTIVES: In children with radiograph fracture-negative lateral ankle injuries, the main objective of this pilot study was to explore the accuracy, sensitivity, and specificity of point-of-care ultrasound (POCUS) performed by a pediatric emergency physician in diagnosing anterior talofibular ligament injuries, radiographically occult distal fibular fractures, and effusions compared with reference standard magnetic resonance imaging (MRI). METHODS: This was a prospective cohort pilot study. Children aged 5 to 17 years with an isolated, acute lateral ankle injury and fracture-negative ankle radiographs were eligible for enrolment. Within 1 week of the injury, enrolled children returned for MRI and POCUS of both ankles. RESULTS: Seven children were enrolled, with a mean age 12.1 (SD, 3.0) years. Overall, POCUS agreed with MRI with respect to anterior talofibular ligament injury in 4 (57%) of 7 cases. Of the 2 cases with MRI-confirmed ligament damage, POCUS accurately identified and graded the extent of ligament damage in 1 case. Point-of-care ultrasound falsely identified ligament injuries in 2 cases. Both imaging modalities confirmed the absence of cortical fractures in all 7 cases. For all findings, POCUS sensitivity and specificity were 57% and 86%, respectively. CONCLUSIONS: In this pilot study, we established that POCUS diagnosed the specific pathology of radiograph-negative lateral ankle injuries with poor sensitivity but good specificity. Thus, POCUS could act as a tool to exclude significant ligamentous and radiographically occult bony injury in these cases. A larger study is needed to validate the utility of POCUS for this common injury.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".