Uncemented Tumor Endoprostheses at the Knee
Bibliographic record
Abstract
UNLABELLED: Although cemented tumor endoprostheses are the most commonly used method for reconstruction of the distal femur or proximal tibia after resection of primary bone tumors, aseptic loosening remains a common complication. Uncemented tumor prostheses may minimize this problem. We investigated the root causes of prosthetic failure for 99 patients with a fixed-hinge, bone-ingrowth Kotz Modular Femur and Tibia Resection System endoprosthesis and compared complications that led to implant failure with results in the literature. Of the 74 distal femoral implants and 25 proximal tibial implants, 25 patients had complications that resulted in prosthetic failure (removal of the prosthesis) at a median of 24.1 (range, 0.8-72.6) months. Failure was caused by prosthesis (n = 18) or oncologic-related (n = 7) complications. However, limb-salvage was possible for 87 of 99 patients. Smaller stem size in the distal femur and longer bone resection length in the proximal tibia were significantly associated with increased risk of prosthetic failure by multivariate analysis. The risk of stem fracture (6 of 99 patients) and infection (10 of 99 patients) was higher than other reports, but aseptic loosening (2 of 99 patients) was uncommon. These results suggest that although the bone-ingrowth surface of this prosthesis leads to a very low aseptic loosening rate, the higher risk of stem fracture and infection must be addressed in future implant designs. LEVEL OF EVIDENCE: Therapeutic study, level IV-1 (case series).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".