Small Improvements in Postoperative Outcome with Gap Balancing Technique Compared with Measured Resection in Total Knee Arthroplasty
Why this work is in the frame
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Bibliographic record
Abstract
Background: There is ongoing debate about how to obtain correct rotational alignment in total knee arthroplasty (TKA). Two commonly used techniques are the measured resection (MR) and the gap balancing (GB) technique. Objective: The objective of the present study was to analyze which of these two techniques confers a clinical advantage up to 10 years postoperatively. Methods: Two hundred patients were randomized to either MR or GB. The primary outcome was the Knee Society Knee Score (KS) 10 years postoperatively. Secondary outcomes were passive range of motion, the Knee Society Function Score (FS), and the Western Ontario and McMasters Universities Osteoarthritis Index (WOMAC), along with implant survival. We employed a two one-sided test (TOST) and linear mixed models to assess clinical outcomes. Results: Mean KS was 82 (95% confidence interval (CI), 80 – 83) and 77 (95% CI, 76 – 79) in the GB and MR group, respectively. The TOST test and linear mixed model both revealed statistical significance (p < 0.001). In addition, GB yielded better postoperative FS and WOMAC. However, between-group differences were consistently small. Implant survival rates at 10 years, with survival for any reason as the endpoint of interest, were 93.7% (95% CI, 86.4% and 97.1%) and 89.8% (95% CI, 81.9% - 94.4%) for the GB group and the MR group, respectively ( p = 0.302). Conclusion: Gap-balancing is a safe and reliable technique. KS for the two study groups at 10 years can be considered equivalent, and the small postoperative advantages may not extend beyond clinical relevance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it