Functional Outcomes of Revision Total Knee Arthroplasty Following Failed Unicompartmental Knee Arthroplasty
Bibliographic record
Abstract
Introduction: Unicompartmental knee arthroplasty (UKA) can be used to treat medial compartment osteoarthritis of the knee. Some of these knees will eventually fail, and need to be revised. There is controversy about using UKA in younger patients as a definitive procedure or as a means to delay total knee arthroplasty (TKA) because the outcomes of subsequent revision surgery may be inferior to a primary TKA. Methods: We retrospectively reviewed a series of 46 revision TKA patients following failed UKA (UKA revisions) using functional outcomes questionnaires and compared the results with a cohort of age and gender matched primary TKA patients. Our hypothesis was that UKA revision surgery would be inferior to primary TKA surgery. Results: Data was collected on 33 knees after a mean follow-up period of five years. There was no significant difference in the Oxford Knee Score (33.7 vs 37.1, p = 0.09) or the Western Ontario and MacMasters Universities Arthritis Index (WOMAC) (24.8 vs. 19.1, p = 0.22). A subgroup analysis demonstrated that UKAs, which fail early, are more likely to produce an inferior outcome following revision surgery than those that survive more than five years. Discussion: We conclude that UKA can be used effectively in appropriately selected patients, as the functional outcome of their subsequent revision to TKA is not significantly inferior to a primary TKA.Keywords: unicompartmental knee arthroplasty, revision knee arthroplasty
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".