Syndesmotic Injury Assessment With Lateral Imaging During Stress Testing in a Cadaveric Model
Bibliographic record
Abstract
BACKGROUND: External rotation, lateral, and sagittal stress tests are commonly used to diagnose syndesmotic injuries, but their efficacy remains unclear. The purpose of this study was to characterize applied stresses with fibular motion throughout the syndesmotic injury spectrum. We hypothesized that sagittal fibular motion would have greater fidelity in detecting changes in syndesmotic status compared to mortise imaging. METHODS: Syndesmotic instability was characterized using motion analysis during external rotation, lateral, and sagittal stress tests on cadaveric specimens (n = 9). A progressive syndesmotic injury was created by sectioning the tibiofibular and deltoid ligaments. Applied loads and fibular motion were synchronously measured using a force transducer and motion capture, respectively, while mortise and lateral radiographs were acquired to quantify clinical measurements. Fibular motion in response to these 3 stress tests was compared between the intact, complete lateral syndesmotic injury and lateral injury plus a completely sectioned deltoid condition. RESULTS: Stress tests performed under lateral imaging detected syndesmotic injuries with greater sensitivity than the clinical-standard mortise view. Lateral imaging was twice as sensitive to applied loads as mortise view imaging. Specifically, half as much linear force generated 2 mm of detectable syndesmotic motion. In addition, fibular motion increased linearly in response to sagittal stresses (Pearson's r [ρ] = 0.91 ± 0.1) but not lateral stresses (ρ = 0.29 ± 0.66). CONCLUSION: Stress tests using lateral imaging detected syndesmotic injuries with greater sensitivity than a typical mortise view. In addition to greater diagnostic sensitivity, reduced loads were required to detect injuries. CLINICAL RELEVANCE: Syndesmotic injuries may be better diagnosed using stress tests that are assessed using lateral imaging than standard mortise view imaging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".