Revision of a Medial UKA to a Kinematic Aligned TKA: Comparison of Operative Complexity, Postoperative Alignment, and Outcome Scores to a Primary TKA
Bibliographic record
Abstract
Abstract Revision of a medial unicompartmental knee arthroplasty (UKA) to a mechanically aligned total knee arthroplasty (MA TKA) is inferior to a primary TKA; however, revision with kinematic alignment (KA) has not been well studied. The present study determined whether patients revised with KA had a higher use of revision components, different postoperative alignment, and different clinical outcome scores from patients with a primary KA TKA. From 2006 to 2017, all patients suitable for a revision of a failed medial UKA to a TKA and a primary TKA were treated with KA. Reasons for the revision performed in ten females and six males at a mean age 67 ± 8 years included progression of osteoarthritis in the lateral hemi-joint (n = 6), aseptic loosening (n = 4), unremitting medial pain without loosening (n = 4), and insert wear (n = 2). Patients with a revision were matched 1:3 with a control cohort treated with a primary KA TKA. Revisions were performed with primary components without augments, stem extensions, or bone grafts. Seven postoperative alignment parameters of the limb and components were comparable to the control cohort (p > 0.05). At a mean follow-up of 5 years (1–10), implant survival was 100%, and the revision/primary group clinical outcome scores were 39/43 points for the Oxford Knee Score (OKS), 2.2/1.0 cm for the Visual Analog Pain Score, and 12/7 points for the Western Ontario and McMaster Universities Osteoarthritis Index score. When compared with primary KA TKA, surgeons that revise a failed medial UKA to a TKA with use of KA can expect similar operative complexity, comparable postoperative alignments, and a mean OKS of 39 points, which is higher than the mean 27 to 30 point range reported for revision of a failed UKA to a TKA with the use of MA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".