Causes and Clinical Outcomes of Revision Total Knee Arthroplasty
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate causes and clinical outcomes of revision total knee arthroplasty (TKA) with a minimum 2-year follow-up. MATERIALS AND METHODS: We performed a retrospective review on the records of 225 revision TKAs performed from 2003 to 2012 at a single institution. Finally, 206 cases were conducted. To evaluate clinical outcomes, we checked the range of motion (ROM), Knee Society (KS) score, Hospital for Special Surgery (HSS) score, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score preoperatively, 6 months postoperatively and yearly thereafter and complications after revision TKA. RESULTS: The causes of revision TKA were septic complications in 120 and aseptic complications in 86. The main aseptic complication was periprosthetic fracture in 36, followed by 25 loosening, 13 polyethylene wear and 9 instability. At the final follow-up, patients showed improvements in ROM and KS, HSS and WOMAC scores. Of the total, 7 patients underwent re-revision due to reinfection in 6 and periprosthetic fracture in 1. CONCLUSIONS: Due to development of implants and surgical techniques, mechanical complications such as aseptic loosening and instability that need a revision decreased whereas septic complications relatively increased. Therefore, we should be cautious for the occurrence of infection. Overall, revision TKA provided clinical improvement in knee function and patient satisfaction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".