Wear and Osteolysis Around Total Knee Arthroplasty
Bibliographic record
Abstract
Osteolysis induced by wear debris of ultra-high-molecular-weight polyethylene has emerged as a significant problem after total knee arthroplasty. The generation of polyethylene wear and the development of osteolysis around total knee arthroplasty are caused by a combination of patient, implant, and surgical factors. Activity level over time may be the most important patient factor affecting the loads placed on a total knee replacement, but it is the most difficult to manage. Multiple factors related to the manufacturing of the polyethylene implant influence the extent of wear, and surgeons should be cautious in considering enhanced polyethylenes pending results of further investigations. The optimal design of the articular bearing surface remains controversial but needs to be considered with respect to the stresses imparted on component-bone and modular tibial backside interfaces. Surgical factors, including restoration of alignment and ligament balance, are important for long-term durability of the implant. Methods of measuring the wear of total knee implants are still evolving. Thus, when confronted with a worn total knee implant and developing osteolysis, the surgeon should consider each of these factors in selecting the best management option to eliminate the source of debris and minimize the potential for wear and osteolysis following revision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.003 | 0.002 |
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".