The impact of soft tissue release on radiological and clinical outcomes in functional alignment robot-assisted total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: To assess the impact of soft tissue release (STR) on radiological and clinical outcomes in functional alignment robot-assisted total knee arthroplasty (FA-TKA). METHODS: This retrospective controlled study enrolled a total of 127 patients who underwent Mako robot-assisted FA-TKA. Based on whether soft tissue release was performed, patients were categorized into an STR group (n = 38) and a non-STR group (n = 89). Radiographic parameters, including the hip-knee-ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), medial proximal tibial angle (MPTA), and posterior tibial slope (PTS), were assessed. Knee function was evaluated using the Knee Society Score (KSS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) preoperatively and at 3 and 6 months postoperatively. RESULTS: STR was required in 50% of knees with valgus deformity and 28.9% of knees with varus deformity. Rates increased significantly with deformity severity: 0% for varus < 10°, 62.5% for varus ≥ 10°, 95.8% for varus ≥ 15°. In knees with varus deformity, 68.6% medial collateral ligament (MCL) and 94.3% posterior cruciate ligament (PCL) were most commonly released; iliotibial band (ITB) release was universal in knees with valgus deformity requiring STR. Preoperative HKA and MPTA were significantly lower, while mLDFA was higher, in the STR group compared to the non-STR group (all P < 0.05). Postoperatively, HKA remained lower in the STR group compared to the non-STR group. There were no significant differences between groups in postoperative mLDFA, MPTA, PTS, or in KSS and WOMAC scores at any time point. Notably, the magnitude of correction in HKA and MPTA was greater in the STR group. CONCLUSION: During FA-TKA, STR is commonly employed to achieve soft tissue balance in cases of severe varus (≥ 10°) or knees with valgus deformity. In knees with varus deformity, the most frequently released structures are the MCL and PCL, whereas in knees with valgus deformity, release predominantly involves the ITB. However, intraoperative STR did not significantly impact postoperative lower limb alignment or short-term clinical outcomes compared to no release. The decision to perform STR should be individualized based on the type and severity of preoperative deformity.
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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 | 0.005 |
| 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".