2D vs. 3D Ultrasound Diagnosis of Pediatric Supracondylar Fractures
Bibliographic record
Abstract
Supracondylar fractures are common injuries in children. Diagnosis typically relies on radiography, which can involve long wait times in the ED, emits ionizing radiation, and can miss non-displaced fractures. Ultrasound (US) has the potential to be a safer, more convenient diagnostic tool, especially with new highly portable handheld 2D point of care US (POCUS). This study aimed to determine the reliability of 2D POCUS for the detection of supracondylar fractures and elbow joint effusions, to contrast the accuracy of 2D POCUS vs. 3DUS vs. radiographs, and to determine whether blinded image interpretation could produce similar results to non-blinded real-time imaging. Fifty-seven children were scanned with 2D POCUS and 3DUS on the affected elbow. US scans were then read by three blinded readers, and the results were compared to gold-standard radiographs. Compared to a gold standard of 30-day radiographic diagnosis, readers of 2D POCUS detected supracondylar fracture and effusion with sensitivities of 0.91 and 0.97, respectively, which were both higher than with 3DUS. Inter-rater reliability of fracture detection was moderate for 2D POCUS (k = 0.40) and 3DUS (k = 0.53). Consensus sensitivities, although high, were lower than reports from some non-blinded studies, indicating that clinical presentation serves as an important factor in detection rates. Our results from consensus US diagnosis support the validity of using 2D POCUS in children for supracondylar fracture and elbow effusion diagnosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".