Cementless Versus Cemented Fixation in Total Knee Arthroplasty: Analysis of Regional Tibial Bone Density and Clinical Outcome
Bibliographic record
Abstract
Background: Cementless fixation in total knee arthroplasty (TKA) has theoretical advantages of being biological and bone preserving, but some surgeons are less confident in using it given previous reports of high failure rates in some implant designs. This study aimed to investigate the effect of fixation method on tibial bone density, clinical outcome, and survivorship. The main research question was whether fixation method would affect the postoperative change in tibial bone density in TKA. Methods: This study analyzed 53 cementless TKAs and 53 cemented TKAs of the same brand (Triathlon, Stryker). Digital radiological densitometry (DRD) was used to quantify the changes in regional tibial bone density (RTBD) within the first 2 years. Clinical outcome scores and survivorship were recorded. Results: = 0.029). Clinical outcome scores (Knee Society Score, Western Ontario and McMaster Universities Osteoarthritis Index, and Forgotten Joint Score) were similar. No case of aseptic loosening was reported. Conclusions: Proximal tibial bone resorption was common in both cementless and cemented TKAs. Cementless fixation preserved more tibial metaphyseal bone globally at 6 months and at the lateral tibial condyle at 24 months. Its early clinical outcomes and survivorship were comparable to those of cemented fixation.
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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 | 0.000 | 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) | 0.001 | 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".