Evaluation of a 3D-printed total knee arthroplasty using radiostereometric analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it