In Vitro Quantification of Wear in Tibial Inserts Using Microcomputed Tomography
Bibliographic record
Abstract
BACKGROUND: Wear of polyethylene tibial inserts can decrease the longevity of total knee arthroplasty. Wear is currently assessed using laboratory methods that may not permit backside wear measurements or do not quantify surface deviation. QUESTIONS/PURPOSES: We developed and validated a technique to quantify polyethylene wear in tibial inserts using microcomputed tomography (micro-CT), a nondestructive high-resolution imaging technique that provides detailed images of surface geometry in addition to volumetric measurements. METHODS: Six unworn and six wear-simulated polyethylene tibial inserts were evaluated. Each insert was scanned three times using micro-CT at a resolution of 50 μm. The insert surface was reconstructed for each scan and the insert volume was calculated. Gravimetric analysis was performed for all inserts, and the micro-CT and gravimetric volumes were compared to determine accuracy. We created three-dimensional surface deviation maps. RESULTS: Micro-CT generated high-quality three-dimensional renderings of the insert surface geometry. Between-scan precision was 0.07%; we observed no difference between micro-CT and gravimetric volume measurements. CONCLUSIONS: Micro-CT can provide precise and accurate volumetric measurements in addition to quantifiable three-dimensional surface deviation maps for the entire insert surface. The technique has the potential to evaluate wear in wear simulator trials and retrieval studies. CLINICAL RELEVANCE: This micro-CT technique combines the benefits of volumetric and surface scanning methods to quantify wear across all surfaces of polyethylene components with a single tool. When applied in wear simulator and retrieval studies, these measurements can be used to evaluate and predict the wear properties of the components.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".