Aseptic loosening is associated with medial tilting and anterior translational migration of the tibial implant in mechanically aligned total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Aseptic loosening is a significant cause of implant revision in total knee arthroplasty, and radiostereometric analysis has been used to predict loosening by measuring implant migration over time relative to its position at the time of the index surgery. Studies have suggested that analyzing specific migration patterns may improve prediction of loosening, compared to using measures of the Maximum Total Point Motion alone. Therefore, the objective of this study was to determine whether patients monitored using radiostereometric analysis who experienced either aseptic loosening or revision exhibited distinctive tibial implant migration patterns. METHODS: Extending a previous study using radiostereometric analysis, we calculated the 6-degree-of-freedom tibial implant migration patterns for seven patients with cemented mechanically aligned total knee arthroplasty implants who either developed aseptic loosening or were candidates for revision. We used simple linear regression to identify trends over time. FINDINGS: /month, p < 0.001) and anterior translation (b = 0.67 mm/month, p = 0.005). INTERPRETATION: Our study showed two statistically detectable migration trends associated with tibial component aseptic loosening. Although we were unable to assess in this study whether focusing on migration patterns in these directions provides greater predictive value than using Maximum Total Point Motion, the results suggest that certain migration mechanisms are more prevalent than others, which could motivate further research into the causes of such migration patterns.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".