Technique to Quantify Subsurface Cracks in Retrieved Polyethylene Components Using Micro-CT
Bibliographic record
Abstract
No current method to study delamination and subsurface cracking in polyethylene joint replacement components provides accurate, nondestructive, and quantitative measurements. A technique to study damage both at and below the surface could be of great benefit. We report the development of a micro-CT technique to nondestructively examine and quantify subsurface cracking in retrieved polyethylene tibial inserts. Five severely delaminated inserts and two never-implanted inserts were obtained from our institution's implant retrieval library and scanned with micro-CT. The insert subsurface was examined for cracks, and their location and widths were measured using a digital line tool. Subsurface cracks were readily apparent only in the images of the delaminated inserts. Cracks ran horizontally, just below the articular and back-side surfaces, and vertically at the center and lateral edges of the inserts and at the tibial post. Cracks ranged from 0.12 to 6.01 mm below the surface, with widths of 0.06-0.97 mm. Micro-CT can nondestructively visualize and quantify subsurface cracks. This is an enhancement to its previously reported use to quantify surface deviations from wear. Micro-CT is well suited for longitudinal pin-on-disk and wear simulator trials, in addition to studies of retrieved components.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".