Anatomic Versus Mechanically Aligned Total Knee Arthroplasty for Unicompartmental Knee Arthroplasty Revision
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to compare the intra-operative benefits and the clinical outcomes from kinematic or mechanical alignment for total knee arthroplasty (TKA) in patients undergoing revision of failed unicompartmental kneel arthroplasty (UKA) to TKA. METHODS: Ten revisions were performed with a kinematic alignment technique and 11 with a mechanical alignment. Measurements of the hip-knee-ankle angle (HKA), the lateral distal femoral angle (LDFA), and the medial proximal tibial angle (MPTA) were performed using long-leg radiographs. The need for augments, stems, and constrained inserts was compared between groups. Clinical outcomes were compared using the WOMAC score along with maximum distance walked as well as knee range of motion obtained prior to discharge. All data was obtained by a retrospective review of patient files. RESULTS: The kinematic group required less augments, stems, and constrained inserts than the mechanical group and thinner polyethylene bearings. There were significant differences in the lateral distal femoral angle (LDFA) and the medial proximal tibial angle (MPTA) between the two groups (p<0.05). The mean WOMAC score obtained at discharge was better in the kinematic group as was mean knee flexion. At last follow up of 34 months for the kinematic group and 58 months for the mechanical group, no orthopedic complications or reoperations were recorded. CONCLUSION: Although this study has a small patient cohort, our results suggest that kinematic alignment for TKA after UKA revision is an attractive method. Further studies are warranted.
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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 | 0.000 | 0.004 |
| 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) | 0.002 | 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".