A prospective randomized study comparing navigation versus conventional total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Navigation is associated with improved accuracy in alignment. However, its influence on clinical outcome is inconclusive. The aim of this study was to compare the component alignment and functional outcome in patients undergoing navigation-assisted and conventional total knee replacement (TKR). MATERIALS AND METHOD: A prospective randomized study consisting of two groups (group A and group B) was carried out. Group A consisted of patients undergoing TKR using conventional jig-based method, whereas group B consisted of patients undergoing TKR using computer navigation-assisted method. We measured and compared the coronal and sagittal plane alignment in X-ray and rotational alignment in computed tomography scan between both groups. Functional outcome was analysed using Knee Society Score (KSS) and Western Ontario and McMaster University scale (WOMAC) score. RESULTS: A total of 50 patients were randomized into two groups A and B each with 25 patients. Navigation was associated with more accuracy in mechanical axis alignment ( p = 0.011) and femoral component rotation ( p = 0.033). The mean follow-up was 4.6 years (range 48-62 months). There was no statistically significant difference between the groups with respect to KSS and WOMAC score at the minimum follow-up of 4 years. CONCLUSION: We concluded that even though navigation-assisted system is associated with better accuracy, there was no difference in clinical outcome at an average follow-up of 4.6 years.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".