Patella scores are similar both with gap balancing and measured resection after total knee arthroplasty: a randomized single‐centre study
Bibliographic record
Abstract
PURPOSE: The purpose of this prospective study was to compare femoral component rotation (FCR) values when adjusted with 'gap balancing' (GB) and 'measured resection' (MR) techniques following total knee arthroplasty (TKA). The study hypothesis was that the GB technique would be better on FCR than MR in TKA. METHODS: From a total of 93 unilateral TKAs performed between August 2019 and November 2020, the FCR values were adjusted by GB in 46 cases and MR in 47. Post-TKA magnetic resonance imaging (MRI) was applied for FCR assessment. Orthoroentgenograms and lateral knee radiographs were taken to determine the mechanical axis and posterior condylar offset (PCO) ratio, respectively. Both groups were compared radiologically. The Western Ontario and Mcmaster Universities Osteoarthritis Index (WOMAC), Knee Society Score (KSS), and Hospital for Special Surgery (HSS) patella scores were calculated and compared between the groups preoperatively and at the end of 6 months, and 1 and 2 years postoperatively. RESULTS: There was no difference between the groups in respect of the demographic data. The mean HSS patella score was 86.4 ± 4.1 in the GB group and 84.6 ± 3.8 in the MR group in the 2nd year (p = 0.047). A higher degree of external rotation in the FC was determined in the GB group [2.2° (1.7°-4.3°)] compared to the MR group [1.7° (0.8°-3.0°)] (p = 0.009). The postoperative increase in PCO ratio was higher in the GB group (p = 0.005). All other variables were similar in both groups. CONCLUSION: The results of this study showed that at the end of the 2nd year, the HSS patella scores were better, FCs were more externally rotated and PCO ratios were higher in TKAs using the GB technique. However, taking into account that the difference between the 2nd year HSS patella scores was too small to be considered clinically significant, it was shown that both the GB and MR techniques can be used for FCR in clinical practice without any hesitation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".