Image-based robotic (ROSA® knee system) total knee arthroplasty with inverse kinematic alignment compared to conventional total knee arthroplasty
Bibliographic record
Abstract
INTRODUCTION: In 2020, 368 million people globally were affected by knee osteoarthritis, and prevalence is projected to increase with 74% by 2050. Relatively high rates of dissatisfactory results after total knee arthroplasty (TKA), as reported by approximately 20% of patients, may be caused by sub-optimal knee alignment and balancing. While mechanical alignment has traditionally been the goal, patient-specific alignment strategies are gaining interest. Robotic assistance could potentially facilitate implementation of these alignment strategies through data-based surgical planning, accurate execution of the surgical plan and validation. The clinical value of surgical assistance in diverging from mechanical alignment remains to be investigated. In the present study, robotic-assisted TKA will be performed to pursue inverse kinematic alignment (iKA) within predefined boundaries, focusing on restoring native tibia joint line. METHODS: Knee System, Zimmer Biomet, Montreal, Quebec, Canada) aiming for iKA compared to conventional TKA aiming for mechanical alignment. A total of 150 participants will be randomized (1:1) to either treatment to provide an 80% power for a 4.8-point clinically important difference in the primary outcome measure, the Oxford Knee Score (OKS) 12 months after surgery. Allocation was achieved using computer-based randomization. Outcomes will be analyzed using linear mixed models with time and group as main factors and interaction-term. Secondary outcomes include clinical metrics (leg alignment, implant and patient survival), surgical parameters (adverse events, surgery duration, blood loss, hospital stay length, medication use), patient-reported outcomes (symptoms, quality of life, pain), mobility and physical activity measurements, metabolic syndrome, cost-efficacy, and gait and continuous glucose monitoring. ETHICS AND DISSEMINATION: This study has been approved by the Medical Ethical Committee Zuyd and Zuyderland Medical Centre (NL79161.096.21/METCZ20220006), September 2022. TRIAL REGISTRATION NUMBER: NCT05685693 (clinicaltrials.gov).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".