Total Hip Wear Assessment: A Comparison Between Computational and In Vitro Wear Assessment Techniques Using ISO 14242 Loading and Kinematics
Bibliographic record
Abstract
In the present study a direct comparison was made between in vitro total hip wear testing and a computational analysis considering the effects of time and a nonlinear stress-strain relationship for ultrahigh molecular weight polyethylene (UHMWPE) at 37 degrees C. The computational simulation was made correct through calibration to experimental volumetric wear results, and the predicted damage layout on the acetabular liner surface was compared with results estimated from laser scanning of the actual worn specimens. The wear rates for the testing specimens were found to be 17.14+/-1.23 mg/10(6) cycles and 19.39+/-0.79 mg/10(6) cycles, and the cumulative volumetric wear values after 3x10(6) cycles were 63.70 mm(3) and 64.02 mm(3) for specimens 1 and 2, respectively. The value of the calibrated wear coefficient was found to be 5.32(10(-10)) mm(3)/N mm for both specimens. The major difference between the computational and experimental wear results was the existence of two damage vectors in the experimental case. The actual location of damage was virtually the same in both cases, and the maximum damage depth of the computational model agreed well with the experiment. The existence of multiple wear vectors may indicate the need for computational approaches to account for multidirectional sliding or strain hardening of UHMWPE. Despite the limitation in terms of describing the overall damage layout, the present computational model shows that simulation can mimic some of the behavior of in vitro wear.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".