Robotics versus personalized 3D preoperative planning in total knee arthroplasty: a propensity score-matched analysis
Bibliographic record
Abstract
PURPOSE: Lower limb alignment is crucial in total knee arthroplasty (TKA). Previous studies have shown that robotics and personalized three-dimensional (3D) preoperative planning could improve postoperative alignment accuracy compared with conventional TKA, but comparison between the above two techniques has never been reported. The authors hypothesized that robotics may be superior to personalized 3D preoperative planning in terms of postoperative alignment in primary TKA, with similar patient-reported outcome measures (PROMs) but higher cost and longer operative time. METHODS: A consecutive series of patients who received TKA in our center from September 2020 to January 2021 were enrolled retrospectively. After 1:2 matching, 52 and 104 patients were included and divided into study group for robotics and control group for personalized 3D preoperative planning, respectively. Multiple postoperative alignment angles were measured, and clinical features such as operation and tourniquet time, length of hospital stay and hemoglobin (Hb) were recorded. Knee Society Score (KSS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were used to evaluate clinical results. RESULTS: Compared with control group, robotics group had significantly lower frontal femoral component angle (FFC) and frontal tibial component angle (FTC) absolute deviation (P < 0.05). It also had less outliers in hip-knee-ankle angle (HKA), FTC, lateral femoral component angle (LFC) and lateral tibial component angle (LTC) (P < 0.05). Hb loss of robotics group was significantly lower than control group (P < 0.001), while the operation and tourniquet time were longer (P < 0.001). There was no significant difference in KSS and WOMAC scores between two groups. CONCLUSION: Compared with control group, patients in robotics group had significantly less malalignment, malposition, Hb loss, but similar PROMs. The operations in robotics group spent longer operation time and cost more compared with control group. TRIAL REGISTRATION: The Chinese Clinical Trial Registry, ChiCTR2000036235. Registered 22 August 2020, http://www.chictr.org.cn/showproj.aspx?proj=59300 . LEVEL OF EVIDENCE: III.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".