Ultrasound Assessment of Ankle Syndesmotic Injuries in a Pediatric Population
Bibliographic record
Abstract
OBJECTIVE: To determine sensitivity and specificity for anterior-inferior tibiofibular ligament (AiTFL) integrity and tibiofibular clear-space (TFCS) cut-off points for dynamic evaluation using ultrasound (US) in a pediatric population. DESIGN: Prospective cohort study. SETTING: Tertiary care university-affiliated pediatric hospital patients between the ages of 12 and 18 sustaining acute ankle trauma with syndesmotic injury. INTERVENTIONS: Participants were assigned to the syndesmotic injury protocol that included a standardized MRI and US. MAIN OUTCOME MEASURES: Anterior-inferior tibiofibular ligament integrity for static assessment and TFCS measurements for dynamic assessment on US. For dynamic assessment, the distance between the distal tibia and fibula was first measured in neutral position and then in external rotation for each ankle. The US results on AiTFL integrity were compared with MRI, considered as our gold standard. Optimal cut-off points of TFCS values were determined with receiver operating characteristics curve analysis. RESULTS: Twenty-six participants were included. Mean age was 14.8 years (SD = 1.3 years). Sensitivity and specificity for AiTFL integrity were 79% and 100%, respectively (4 false negatives on partial tears). For dynamic assessment, the cut-off points for the differences in tibiofibular distance between the 2 ankles in 1) neutral position (TFCS N I-U ) and 2) external rotation (TFCS ER I-U ) were 0.2 mm (sensitivity = 83% and specificity = 80%) and 0.1 mm (sensitivity = 83% and specificity = 80%), respectively. CONCLUSIONS: Static US could be used in a triage context as a diagnostic tool for AiTFL integrity in a pediatric population as it shows good sensitivity and excellent specificity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".