Evaluation of a 3D-printed total knee arthroplasty using radiostereometric analysis
Bibliographic record
Abstract
Aims Our intention was to investigate if the highly porous biological fixation surfaces of a new 3D-printed total knee arthroplasty (TKA) achieved adequate fixation of the tibial and patellar components to the underlying bone. Patients and Methods A total of 29 patients undergoing primary TKA consented to participate in this prospective cohort study. All patients received a highly porous tibial baseplate and metal-backed patella. Patient-reported outcomes measures were recorded and implant migration was assessed using radiostereometric analysis. Results Patient function significantly improved by three months postoperatively (p < 0.001). Mean difference in maximum total point motion between 12 and 24 months was 0.021 mm (-0.265 to 0.572) for the tibial implant and 0.089 mm (-0.337 to 0.758) for the patellar implant. The rate of tibial and patellar migration was largest over the first six postoperative weeks, with no changes in mean tibia migration occurring after six months, and no changes in mean patellar migration occurring after six weeks. One patellar component showed a rapid rate of migration between 12 and 24 months. Conclusion Biological fixation appears to occur reliably on the highly porous implant surface of the tibial baseplate and metal-backed patellar component. Rapid migration after 12 months was measured for one patellar component. Further investigation is required to assess the long-term stability of the 3D-printed components and to determine if the high-migrating components achieve fixation. Cite this article: Bone Joint J 2019;101-B(7 Supple C):40–47
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".