Mass Gain Behaviour of Tibial Polyethylene Inserts during Soak Testing
Bibliographic record
Abstract
Fluid adsorption and the associated mass gain behaviour in tibial inserts of total knee replacements was investigated in polyethylene (PE) manufactured from extruded GUR 1050 resin. Repeatedly removing the PE inserts from the soak fluid for gravimetric assessment (including cleaning, desiccation, and weighing) increased the mass gain. Soaking PE inserts for 46 days or 92 days seemed to give about the same mass gain. PE inserts that were soaked at 37 degrees C gained more mass than PE inserts soaked at room-temperature. Gas-plasma sterilized PE inserts gained less mass than gamma-in-air sterilized PE inserts. No statistically significant differences were detected in mass gain between PE inserts that were of 10mm and 14mm thickness. The mass gain of PE inserts was higher in protein-rich soak fluid compared with low-ion distilled water. Prior to knee simulator wear testing, tibial PE inserts should be conditioned in the same medium and under the same test conditions (gravimetric assessment frequency, fluid protein content, and fluid temperature). This approach would help improve the accuracy and precision of the gravimetrically determined PE wear rate during knee simulator wear testing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".