Complications of Total Hip Arthroplasty: MR Imaging—Initial Experience
Bibliographic record
Abstract
PURPOSE: To investigate the use of standard magnetic resonance (MR) imaging sequences with simple parameter modifications for the detection and characterization of total hip arthroplasty (THA) complications. MATERIALS AND METHODS: An initial phantom study was performed with cobalt-chrome and titanium prostheses to establish the imaging parameters for a subsequent clinical study. In the clinical study, coronal and transverse MR imaging of 14 THA prostheses was performed before and after intravenous contrast material administration in 12 patients who were being considered for revision arthroplasty. The images were reviewed for evidence of juxtaarticular or periprosthetic abnormalities, patterns of contrast enhancement, and quality of periprosthetic tissue depiction. RESULTS: Phantom study results showed improved periprosthetic tissue depiction with use of thin sections, increased frequency-encoding gradient strength, and fast spin-echo sequences. The clinical study results demonstrated periprosthetic abnormalities in 11 cases: mechanical loosening in two cases (including one case with an associated periprosthetic fracture); granulomatosis, eight; and infection, one. In 100% of cases, tissue depiction around the femoral component was judged to be of "diagnostic quality." Tissue depiction around the acetabular component was of diagnostic quality in five (36%) cases. In all seven surgically confirmed cases, a correct diagnosis was made preoperatively with MR imaging. CONCLUSION: By using simple modifications to standard MR imaging sequences, diagnostic-quality MR imaging of THA complications can be performed, particularly around the femoral prosthetic stem.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".