Nondestructive microimaging during preclinical pin-on-plate testing of novel materials for arthroplasty
Bibliographic record
Abstract
The purpose of this study was to determine the ability of micro-computed tomography to quantify wear in preclinical pin-on-plate testing of materials for use in joint arthroplasty. Wear testing of CoCr pins articulating against six polyetheretherketone plates was performed using a pin-on-plate apparatus over 2 million cycles. Change in volume due to wear was quantified with gravimetric analysis and with micro-computed tomography, and the volumes were compared. Separately, the volume of polyetheretherketone pin-on-plate specimens that had been soaking in fluid for 52 weeks was quantified with both gravimetric analysis and micro-computed tomography, and repeated after drying. The volume change with micro-computed tomography was compared to the mass change with gravimetric analysis. The mean wear volume measured was 8.02 ± 6.38 mm(3) with gravimetric analysis and 6.76 ± 5.38 mm(3) with micro-computed tomography (p = 0.06). Micro-computed tomography volume measurements did not show a statistically significant change with drying for either the plates (p = 0.60) or the pins (p = 0.09), yet drying had a significant effect on the gravimetric mass measurements for both the plates (p = 0.03) and the pins (p = 0.04). Micro-computed tomography provided accurate measurements of wear in polyetheretherketone pin-on-plate test specimens, and no statistically significant change was caused by fluid uptake. Micro-computed tomography quantifies wear depth and wear volume, mapped to the specific location of damage on the specimen, and is also capable of examining subsurface density as well as cracking. Its noncontact, nondestructive nature makes it ideal for preclinical testing of materials, in which further additional analysis techniques may be utilized.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".