The John Charnley Award: Diagnostic Accuracy of MRI Versus Ultrasound for Detecting Pseudotumors in Asymptomatic Metal-on-Metal THA
Bibliographic record
Abstract
BACKGROUND: The prevalence of pseudotumors in patients with large-head metal-on-metal (MOM) THA has been the subject of implant recalls and warnings from various regulatory agencies. To date, there is no consensus on whether ultrasound or MRI is superior for the detection of pseudotumors. QUESTIONS/PURPOSES: We prospectively compared ultrasound to MRI for pseudotumor detection in an asymptomatic cohort of patients with MOM THAs. We also compared ultrasound to MRI for assessment of pseudotumor growth and progressive soft tissue involvement at a 6-month interval. METHODS: We enrolled 40 patients with large-head MOM THAs in the study. The mean age was 54 years (range, 34-76 years). The mean time from surgery was 54 months (range, 40-81 months). There were 28 men and 12 women. All patients underwent ultrasound and MRI using slice encoding for metal artifact correction. The gold standard was defined as follows: if both ultrasound and MRI agreed, this was interpreted as concordant and the result was considered accurate. RESULTS: Ultrasound and MRI agreed in 37 of 40 patients (93%). The prevalence of pseudotumors was 31% (12 of 39) in our cohort. Twenty-three of 39 patients (59%) had completely normal tests and four (10%) had simple fluid collections. Ultrasound had a sensitivity of 100% and specificity of 96% while MRI had a sensitivity of 92% and specificity of 100%. CONCLUSIONS: A negative ultrasound rules out pseudotumor in asymptomatic patients as this test is 100% sensitive. Given its lower cost, we recommend ultrasound as the initial screening tool for pseudotumors.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".