Commissioning of a displacement-controlled knee wear simulator and exploration of some issues related to the lubricant
Bibliographic record
Abstract
A six-station displacement-controlled knee simulator with separately controlled left (L) and right (R) banks (three wear implants per bank) was commissioned for a total of three million cycles (Mc) following ISO 14243-3. A commissioning protocol was applied to compare the polyethylene wear among the six wear stations by exchanging the implants between wear stations. Changes in lubricant characteristics during wear testing, such as polypeptide degradation, low-molecular-weight polypeptide concentration, and possible microbial contamination were also assessed. The total mean wear rate for the implants was 23.60 +/- 1.96 mm3/Mc and this was of a similar magnitude to the mean wear rate for the same implant tested under similar conditions by DePuy Orthopaedics Inc. (Warsaw, IN). Repeated run-in wear was observed when the implants were exchanged between wear stations, suggesting that implants should be subjected to the same wear station throughout the duration of a wear test. The total polypeptide degradation for the implants measured 30.53 +/- 3.96 percent; the low-molecular-weight polypeptide concentration of the "used" lubricant for implants (0.131 +/- 0.012 g/L) was 3.3 times greater than the mean polypeptide concentration of the fresh, "unused" lubricant (0.039 +/- 0.004 g/L). This increase in low-molecular weight polypeptide concentration was suggested to be attributable to protein shear in the articulation of the implant, the circulation of the lubricant, and some proteolytic activity. Sodium azide was ineffective in maintaining a sterile environment for wear testing as a single, highly motile Gram-negative micro-organism was identified in the lubricant from wear tests.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".