Regional Measurements of Surface Deviation Volume in Worn Polyethylene Joint Replacement Components
Bibliographic record
Abstract
Total joint replacements can be subject to the loss of polyethylene material due to wear, leading to osteolysis and decreased implant longevity. Micro-computed tomography (micro-CT) techniques have recently been developed to calculate 3D surface deviations in worn implant components. We describe a micro-CT technique to measure the volume of the surface deviations (volume of wear plus creep) within a specific region or compartment, and report its repeatability and reproducibility. Six worn polyethylene tibial inserts were scanned using a laboratory micro-CT scanner and subsequently reconstructed at 50 μm voxel spacing. A previously developed custom software application was used to quantify the 3D surface deviations between the worn tibial inserts and an unworn reference geometry. Three observers (two trained and one expert) used new custom software to manually outline the localized regions of surface deviation (three times for each of the worn inserts) and calculate the volume of the deviations. The overall intraobserver variability in the surface deviation volumes was 3.6% medially and 1.1% laterally. The overall interobserver variability was 4.8% medially and 1.7% laterally. Placement of points in outlining the region of deviation contributed the greatest variability to the measurements. Repeatability and reproducibility of the volume measurements are similar to measurements of total (nonregional) wear volume including a previous micro-CT technique (10%), fluid displacement (4.8%), and radiographic measurements (15.7%). The principles of this technique can likely be used to measure regional wear and creep volume in knee and hip joint replacement components from wear simulator, pin-on-disk, and retrieval studies.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".