Kinematics and Early Clinical Outcomes of Navigated Total Knee Arthroplasty through a Lateral Subvastus Approach
Bibliographic record
Abstract
A procedure with subvastus lateral approach has been utilized routinely on 60 patients, navigation was used due to the reduced exposure of this technique. Purpose of this study was to evaluate pain, function, and implant kinematics at early follow up of this surgical technique. Tibial and femoral implant planning was based on ligament balance, gaps, and intraoperative kinematics. This approach, on pain and function, was verified at early follow- up. KSS and pain score were obtained at pre-op, 1, 3, 12 months. Data were analyzed with ANOVA for KSS and Chi-square for Pain. No intraoperative complications were registered, no patellar tendon lesion or avulsion was noted. Preoperative average leg alignment was 4±6° varus (range 16; -14), corrected to 0° (range 2; -1). Kinematic analysis showed rollback on lateral compartment, while on medial compartment rollback was lower or negligible until 70° of flexion. Less than 5% had a “Fair” or “Poor” KSS score after 3 months. Preop pain was: 41% severe; 50% moderate; 8% mild and 0% none. At 1 month pain was: 2% severe; 18% moderate; 55% mild and 25% none. After 3 months 50% of patients had mild and 50% had no pain. This data was maintained after 1 year, with 31% of patients with mild and 69% of patients no pain (p<0.05). This approach produced promising early outcomes in terms of pain, ROM and knee function, with less than 5% of patients presenting sub-optimal clinical results at 3- months. On symmetrical implant, medial pivot behavior was observed. Medial ligamental envelope preservation and navigated ligament balancing allow to optimize the medial stability and minimize the post-operative pain.
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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 | 0.001 |
| 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".