Fixation of a Trabecular Metal Knee Arthroplasty Component
Bibliographic record
Abstract
BACKGROUND: Uncemented tibial components of total knee replacements have lower survival rates than cemented components. Radiostereometric analysis is a highly accurate, effective tool for investigating new implant designs. The purpose of this study was to compare an uncemented Trabecular Metal tibial component with a conventional cemented stemmed tibial component of the same design. METHODS: Seventy subjects undergoing total knee replacement were randomized to receive either the Trabecular Metal or the cemented tibial component. Radiostereometric analysis of micromotion of the tibial components was performed postoperatively at six, twelve, and twenty-four months, and the maximum total point motion of the implant and three-dimensional translations and rotations were recorded. RESULTS: Follow-up was complete for twenty-eight subjects in the Trabecular Metal group and twenty-one subjects in the cemented group. A subset of the Trabecular Metal components migrated extensively in the postoperative period, but all stabilized by one year and the proportion considered to be at risk for early aseptic loosening was 0.0 (95% confidence interval, 0.0 to 0.12) in the group as a whole. Four cemented components were considered to be at risk for early aseptic loosening (proportion at risk, 0.19; 95% confidence interval, 0.08 to 0.4). CONCLUSIONS: This study suggests that the Trabecular Metal component may be an effective alternative to the standard cemented tibial component.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".