Cemented Versus Hybrid Technique of Fixation of the Stemmed Revision Total Knee Arthroplasty: A Literature Review
Bibliographic record
Abstract
Stems are required during revision total knee arthroplasty to bypass damaged periarticular bone and transfer stress to healthier diaphyseal bone. The mode of stem fixation, whether fully cemented or hybrid, remains controversial. Improvements in surgical technique and implant and instrument technology have improved our ability to deal with many of the challenges of revision total knee arthroplasty. Recent publications that reflect contemporary practice has prompted this review of literature covering the past 20 years to determine whether superiority of one fixation mode over the other can be demonstrated. We reviewed single studies of each type of fixation, studies directly comparing both types of fixation, systematic reviews, international registry data, and studies highlighting the pros and cons of each mode of stem fixation. Based on the available literature, we conclude that using both methods of fixation carries comparable outcomes with marginal superiority of the hybrid fixation method, which is of nonstatistical significance, although on an individual case basis, all fixation methods should be kept in mind and the appropriate method implemented when suitable.
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.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".