Small Improvements in Postoperative Outcome with Gap Balancing Technique Compared with Measured Resection in Total Knee Arthroplasty
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".