Manufacturing lot affects polyethylene tibial insert volume, thickness, and surface geometry
Bibliographic record
Abstract
To perform wear measurements on retrieved joint replacement implants, a reference geometry of the implant's original state is required. Since implants are rarely individually scanned before implantation, a different, new implant of the same kind and size is frequently used. However, due to manufacturing variability, errors may be introduced into these measurements, as the dimensions between the retrieved and reference components may not be exactly the same. The hypothesis of this study was that new polyethylene tibial inserts from different manufacturing lots would demonstrate greater variability than those from the same lot. In total, 12 new tibial inserts of the same model and size were obtained, 5 from the same lot and the remainder from different lots. The geometry of each tibial insert was obtained using microcomputed tomography. Measurements of tibial insert volume, thickness, and three-dimensional surface deviations were obtained and compared between tibial inserts from the same and different manufacturing lots. Greater variability was found for the tibial inserts from different manufacturing lots for all types of measurements, including a fourfold difference in volume variability (p < 0.001) and a maximum of 0.21 mm difference in thickness (p < 0.001). Investigators should be aware of this potential confounding error and take steps to minimize it, such as by averaging together the geometries of multiple new components from different manufacturing lots for use as the reference geometry.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".