A new gap balancing technique with functional alignment in total knee arthroplasty using the MAKO robotic arm system: a preliminary study
Bibliographic record
Abstract
BACKGROUND: Gap tension is an important factor influencing the clinical outcomes of total knee arthroplasty (TKA). Traditional mechanical alignment (MA) places importance on neutral alignment and often requires additional soft tissue releases, which may be related to patient dissatisfaction. Conversely, the functional alignment requires less soft tissue release to achieve gap balance. Conventional gap tension instruments present several shortcomings in practice. The aim of this study is to introduce a new gap balancing technique with FA using the modified spacer-based gap tool and the MAKO robotic arm system. METHODS: A total of 22 consecutive patients underwent primary TKA using the MAKO robotic arm system. The gap tension was assessed and adjusted with the modified spacer-based gap tool during the operation. Patient satisfaction was evaluated post-operatively with a 5-point Likert scale. Clinical outcomes including lower limb alignment, Knee Society Score (KSS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) were recorded before surgery, 3 months and 1 year after surgery. RESULTS: The range of motion (ROM) was significantly increased (p < 0.001) and no patients presented flexion contracture after the surgery. KSS and WOMAC score were significantly improved at 3 months and 1 year follow-up (p < 0.001 for all). During the surgery, the adjusted tibial cut showed more varus than planned and the adjusted femoral cut presented more external rotation than planned (p < 0.05 for both). The final hip-knee-ankle angle (HKA) was also more varus than planned (p < 0.05). CONCLUSIONS: This kind of spacer-based gap balancing technique accompanied with the MAKO robotic arm system could promise controlled lower limb alignment and improved functional outcomes after TKA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".