Navigation-Assisted Total Knee Arthroplasty for a Valgus Knee Improves Limb and Femoral Component Alignment
Bibliographic record
Abstract
The purpose of this study was to evaluate the influence of navigation-assisted surgery on radiographic and clinical outcomes after total knee arthroplasty (TKA) for a valgus knee. The authors identified all patients who underwent TKA for a valgus knee between January 2005 and December 2015. Among 83 conventional TKA cases and 55 navigation-assisted TKA cases, propensity score matching was performed for age, sex, body mass index, and preoperative lower limb mechanical axis. Fifty knees were matched to 50 knees. Each case was evaluated regarding lower limb mechanical axis, mechanical lateral distal femoral angle, medial proximal tibial angle, patellar tilt angle, Western Ontario and McMaster Universities Osteoarthritis Index, Knee Society score, and range of motion. Lower outliers of lower limb mechanical axis (30% vs 8%, P=.008) and mechanical lateral distal femoral angle (24% vs 10%, P=.046) were found in navigation-assisted TKA. However, outliers of medial proximal tibial angle, Western Ontario and McMaster Universities Osteoarthritis Index, Knee Society score, and range of motion were similar between the 2 different surgical techniques. Navigation-assisted surgery is correlated with fewer outliers of postoperative lower limb alignment and femoral component position but not tibial component position in TKA for preoperative valgus knee. Clinical outcomes for navigation-assisted TKA were not superior to those for conventional TKA. [Orthopedics. 2019; 42(2):e253-e259.].
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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) | 0.000 | 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) | 0.001 | 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".