Anterolateral Ankle Impingement: MR Arthrographic Assessment of the Anterolateral Recess
Bibliographic record
Abstract
PURPOSE: To determine the accuracy of magnetic resonance (MR) arthrography in assessing the anterolateral recess of the ankle. MATERIALS AND METHODS: Thirty-two patients with chronic ankle pain prospectively underwent gadolinium-enhanced MR arthrography of the tibiotalar joint. All underwent clinical examination and were included if anterolateral impingement (n = 13) or a control condition (n = 19; suspected osteochondral defect, intraarticular bodies, instability, osteoarthritis) was diagnosed. MR imaging included transverse and coronal T1-weighted and sagittal T2-weighted imaging sequences. Images were prospectively analyzed by two readers blinded to the clinical diagnosis. The anterolateral gutter contour was assessed. MR arthrographic findings were correlated with subsequent arthroscopic appearances. RESULTS: MR arthrographic assessment of the anterolateral soft tissues had an accuracy of 97%, sensitivity of 96%, specificity of 100%, negative predictive value of 89%, and positive predictive value of 100%. Accuracy was 100% with clinical anterolateral impingement, with an arthroscopically confirmed abnormality in 12 cases and a normal appearance in one. Anterolateral soft-tissue thickening was identified at MR arthrography in 11 control cases, with arthroscopic confirmation in all. The remaining cases had normal appearances, with an arthroscopic soft-tissue abnormality in one case and a normal appearance in seven. CONCLUSION: MR arthrography of the tibiotalar joint is accurate in assessing the anterolateral recess of the ankle.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".