Perfusion MRI in hips with metal-on-metal and metal-on-polyethylene total hip arthroplasty
Bibliographic record
Abstract
OBJECTIVES: Hips with metal-on-metal total hip arthroplasty (MoM THA) have a high rate of adverse local tissue reactions (ALTR), often associated with hypersensitivity reactions. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures tissue perfusion with the parameter Ktrans (volume transfer constant of contrast agent). Our purpose was 1) to evaluate the feasibility of DCE-MRI in patients with THA and 2) to compare DCE-MRI in patients with MoM bearings with metal-on-polyethylene (MoP) bearings, hypothesising that the perfusion index Ktrans in hips with MoM THA is higher than in hips with MoP THA. METHODS: In this pilot study, 16 patients with primary THA were recruited (eight MoM, eight MoP). DCE-MRI of the hip was performed at 1.5 Tesla (T). For each patient, Ktrans was computed voxel-by-voxel in all tissue lateral to the bladder. The mean Ktrans for all voxels was then calculated. These values were compared with respect to implant type and gender, and further correlated with clinical parameters. RESULTS: There was no significant difference between the two bearing types with both genders combined. However, dividing patients by THA bearing and gender, women with MoM bearings had the highest Ktrans values, exceeding those of women with MoP bearings (0.067 min(-1) versus 0.053 min(-1); p-value < 0.05) and men with MoM bearings (0.067 min(-1) versus 0.034 min(-1); p-value < 0.001). Considering only the men, patients with MoM bearings had lower Ktrans than those with MoP bearings (0.034 min(-1) versus 0.046 min(-1); p < 0.05). CONCLUSION: DCE-MRI is feasible to perform in tissues surrounding THA. Females with MoM THA show high Ktrans values in DCE-MRI, suggesting altered tissue perfusion kinematics which may reflect relatively greater inflammation.Cite this article: Dr P. E. Beaule. Perfusion MRI in hips with metal-on-metal and metal-on-polyethylene total hip arthroplasty: A pilot stud. Bone Joint Res 2016;5:73-79. DOI: 10.1302/2046-3758.53.2000572.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".