Outcomes Of Total Knee Arthroplasty In Valgus Knees: A Comparative Study Of Surgical Techniques
Bibliographic record
Abstract
Background: Total knee arthroplasty (TKA) for valgus knee deformities presents significant surgical challenges due to soft-tissue imbalance, lateral contractures, and medial insufficiency. This study aimed to compare clinical and radiological outcomes of three surgical techniques : medial parapatellar with lateral release, lateral parapatellar, and robotic-assisted TKA. Methods: In this prospective cohort study, 138 knees from 120 patients with valgus deformity ≥10° were enrolled between January 2017 and December 2022. Patients were randomized into three intervention groups: Group A (medial approach with lateral release), Group B (lateral approach), and Group C (robotic-assisted TKA). Outcomes included intraoperative parameters, functional recovery (Knee Society Score [KSS], Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC], Visual Analog Scale [VAS]), range of motion, radiographic alignment, and complication rates, evaluated over a 24-month follow-up. Results: All groups showed significant postoperative improvement. Group C demonstrated the highest mean KSS (89.4 ± 5.8), lowest WOMAC score (11.8 ± 3.6), and greatest VAS reduction (2.1 ± 0.7; p < 0.01). Robotic TKA achieved superior mechanical axis restoration (mean HKA angle 0.8° ± 1.1°) and avoided formal soft-tissue releases. Complication rates were low and comparable across all groups, and implant survivorship was 100% at 24 months. Conclusion: Robotic-assisted TKA offers enhanced functional recovery, optimal alignment, and lower surgical trauma in valgus knees, particularly in severe deformities. It provides a reliable, reproducible solution with favorable mid-term outcomes, supporting its broader adoption in complex arthroplasty settings.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".