Smaller medial component gaps at deep flexion are associated with better patient‐reported outcomes after robotic‐assisted cruciate‐retaining total knee arthroplasty using an anatomically designed implant
Bibliographic record
Abstract
PURPOSE: Soft tissue balance is essential for optimal function and satisfaction after total knee arthroplasty (TKA). However, the clinical significance of medial gap patterns across the entire range of motion (ROM) remains unclear. This study aimed to examine the association between intraoperative medial gap patterns and post-operative patient-reported outcome measures (PROMs) in robotic-assisted cruciate-retaining (CR) TKA. It was hypothesized that medial gap patterns with smaller deep-flexion gaps relative to mid-flexion or 90° flexion would be associated with superior PROMs. METHODS: This retrospective study included 102 patients (120 knees) who underwent primary robotic-assisted CR TKA. Bone and cartilage resection thickness and simulated gaps were assessed at the planning stage, and the final component gaps were recorded at the trial stage. Medial gap patterns were classified as: 1 (constant), 2 (gradually increasing), 3 (increasing/decreasing) and 4 (gradually decreasing). Objective outcomes were assessed using the Knee Society Score (KSS), and subjective outcomes were evaluated using the Western Ontario and McMaster Universities Arthritis Index (WOMAC), Forgotten Joint Score-12 and the satisfaction score of the 2011 KSS. RESULTS: Ten, 47, 51 and 12 knees were classified into Patterns 1-4, respectively. The resection gaps at flexion and the flexion-extension gap difference were relatively larger in Pattern 2. Objective outcomes were comparable among patterns; however, the PROMs differed significantly. Pattern 3 demonstrated better WOMAC stiffness and KSS satisfaction scores than Patterns 1 and 2. CONCLUSION: Medial gap patterns in which deep-flexion gaps did not exceed those at mid-flexion or 90° flexion were associated with better PROMs in robotic-assisted CR TKA. These findings support the hypothesis that avoiding underestimation of native joint gaps at deep flexion is important for optimizing patient-reported outcomes. LEVEL OF EVIDENCE: Level III.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".