Restoring native posterior tibial slope within 4° leads to better clinical outcomes after cruciate‐retaining robot‐assisted total knee arthroplasty with functional alignment
Bibliographic record
Abstract
PURPOSE: The impact of posterior tibial slope (PTS) on clinical outcomes after robot-assisted total knee arthroplasty (RTKA) with functional alignment (FA) remains unclear. This study aimed to evaluate the effect of PTS on clinical outcomes following RTKA with FA. METHODS: A prospectively collected database was retrospectively reviewed for patients who underwent primary cruciate-retaining RTKA (CR-RTKA) with FA using the MAKO robotic system, with a minimum 1-year follow-up. Knees were categorized into two groups: the small change (SC) group (n = 47) with a small PTS change (<4°) and the large change (LC) group (n = 53) with a large PTS change (≥4°). PTS change was defined as differences between pre- and post-operative PTS values. At the 1-year follow-up, clinical outcomes including Knee Society Score (KSS), Western Ontario and McMaster University Index (WOMAC), Forgotten Joint Score-12 (FJS-12) and post-operative complications were compared between groups. A multiple linear regression analysis was conducted to identify independent factors influencing post-operative WOMAC scores. RESULTS: The SC group demonstrated significantly better post-operative clinical outcomes than the LC group (KSS-functional score: 85.63 ± 13.00 vs. 77.08 ± 18.39, p = 0.026; WOMAC: 10.46 ± 5.36 vs. 16.98 ± 10.69, p < 0.001; FJS-12: 75.94 ± 21.96 vs. 61.37 ± 26.61, p = 0.019). In multivariate regression analysis, PTS change was identified as an independent factor correlated with the post-operative WOMAC score (β: 0.842, p = 0.001). No post-operative complications were observed in either group during the follow-up period. CONCLUSIONS: Restoring the native PTS within 4° leads to better clinical outcomes following CR-RTKA with FA without post-operative complications for up to 1 year. Therefore, efforts to restore the native PTS are critical for achieving optimal short-term clinical outcomes after CR-RTKA with FA. LEVEL OF EVIDENCE: Level III.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".