Computer Assisted Navigation Surgery Obviates the Need of Extensive Soft Tissue Release During Total Knee Arthroplasty for Varus Knees
Bibliographic record
Abstract
Introduction: We aim to (a) evaluate the kinematic patterns of varus deformities in patients with osteoarthritic knees (b) compare our result with literature published in the past and identify a valid classification scheme for the kinematic patterns of varus deformity, and (c) validate the concept of need-based soft tissue release in TKA. Methods: The computer navigations data for all those patients who underwent TKA for varus deformity was extracted and was used to assess the change in coronal plane alignment throughout the range of motion. Clinically, patient satisfaction levels were assessed by using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score and functional outcomes were assessed using the Knee Society Score (KSS). Results: 120 patients satisfied the inclusion criteria set for the study. The mean age of the patients was 62.5 ± 7.09 years. The mean preoperative varus deformity was 9.5º ± 4.99o, which was correctable to a mean of 2.88º ± 2.48o in full extension. It was found that 74 (61.7%) patients had correctable varus while 46 (38.3%) had non-correctable varus. Based on the deformity pattern, the soft tissue release required was different in each group of patients. There was a statistically significant improvement both the clinical parameters (p < 0.001). Conclusion: Computer Assisted Navigation System helps to identify the dynamic nature of knee deformity before bone resections and soft tissue release. The soft tissue release for balancing the knee in varus osteoarthritic knees can be tailored sequentially according to need-base for a given pattern of 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 | 0.000 | 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".