The biochemical characteristics of wear testing lubricants affect polyethylene wear in orthopaedic pin-on-disc testing
Bibliographic record
Abstract
Lubricant protein concentration is known to affect crosslinked polyethylene wear in in vitro testing; however, the biochemical nature of these lubricants may also have a significant effect on wear and dictate its clinical relevance. A modified approach to pin-on-disc testing was implemented to explore the effect of four biochemically different lubricants on the wear of two types of crosslinked polyethylene materials (XLK™ and Marathon™; DePuy Synthes, Warsaw, IN, USA). XLK was associated with higher wear rates than Marathon. In comparison to lubricants containing deionized water, lubricants containing phosphate buffered saline solution and hyaluronic acid increased osmolality by up to 1.2 times and thermal stability by up to 1.4 times. This biochemical change reduced wear by up to 12.5 times. Wear rates for XLK and Marathon differed by a factor of 3.2 using lubricants with phosphate buffered saline solution as the dilutive media, but only 2.0 for lubricants with deionized water. Interestingly, varying the concentration of hyaluronic acid did not have a significant effect on wear, and differences between XLK and Marathon wear rates were not found to be statistically significant when hyaluronic acid was added to the lubricant. The findings of this study showed that increasing the osmolality and thermal stability of lubricants to more clinical levels decreased wear; however, the effect of hyaluronic acid on wear may not be apparent in simplistic pin-on-disc testing. It was suggested that phosphate buffered saline solution be used as the dilutive media of choice in order to better differentiate the ranking of materials while maintaining some clinical relevance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".