Does the gap balance technique really elevate the joint line in total knee arthroplasty? A single-center, randomized study
Bibliographic record
Abstract
OBJECTIVES: This study aims to compare patients undergoing total knee arthroplasty (TKA) with gap balancing (GB) versus measured resection (MR) techniques in terms of joint line (JL) using radiographic measurements from both femoral and tibial sides. PATIENTS AND METHODS: Between August 2019 and May 2021, a total of 107 patients who underwent TKA were included in this randomized study. The patients were divided into two groups as the GB group (n=54; 9 males, 45 females; mean age: 66.6±7.4 years; range, 51 to 81 years) and the MR group (n=53; 10 males, 43 females; mean age: 64.0±6.8 years; range, 50 to 80 years). The adductor tubercle joint line (ATJL) and the tibial tubercle joint line (TTJL) were evaluated for JL measurement. Clinical and functional evaluation was made using the range of motion of the joint, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, Knee Society Score (KSS)-Knee and Functional scores. RESULTS: The mean follow-up was 34.2±3.5 months in the GB group and 34.4±3.3 months in the MR group (p=0.80). The mean operation time was 119.1±14.9 min in the GB group and 118.6±17.5 min in the MR group (p=0.89). A total of 31 (57.4%) patients in the GB group had a degree of release of 3-4, while 21 (39.6%) patients in the MR group had a degree of release of 3-4 (p=0.26). The ATJL measurement was similar in the GB and MR groups, while the TTJL measurement was significantly different between the two groups (p=0.01). There was no significant correlation between the ATJL measurement and the degree of release, while there was a significant correlation between the TTJL and the degree of release (r=0.731, p<0.001). CONCLUSION: While ATJL measurements in TKA showed similar results with GB and MR techniques, the amount of release may have caused the significantly higher JL elevation in the GB group in TTJL measurements. Based on these findings, we suggest that radiographic JL measurements on both the tibial and femoral sides in TKA may provide a more accurate assessment and we recommend to measure JL from the femoral side.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".