Short-term efficacy and learning curve of robot-assisted total knee arthroplasty
Bibliographic record
Abstract
Objective To retrospectively analyze the short-term efficacy and learning curve characteristics of SkyWalker orthopedic robot (hereinafter referred to as robot) assisted total knee arthroplasty (TKA). Methods A total of 60 patients (60 knees) who underwent TKA for end-stage knee osteoarthritis in our center from October 2020 to February 2021 were enrolled in this study. They were divided into the robotic group (30 knees) and conventional group (30 knees). The scores of Western Ontario and McMaster University Osteoarthritis Index (WOMAC), imaging indexes, and operation-related indexes before surgery and 3, 6 months after surgery were studied and compared between the 2 groups. The learning curve-related indexes of the robot-assisted TKA group and conventional were compared; the short-term efficacy during the learning curve period between the 2 groups was compared. Results The total WOMAC score of the robotic group was better than that of the conventional group at 3 months after surgery (P < 0.05), and there was no statistical difference in the score at 6 months after surgery. The postoperative hip-knee-ankle (HKA) angeland frontal tibia component (FTC) angle of the robotic group were better than those of the conventional group (P < 0.05); the learning curve of the robotic group was 15 cases, and the learning curve indexes after 15 cases were significantly reduced (P < 0.05); the operation time of the robotic group during the learning curve period was longer than the conventional group (P < 0.05); intraoperative blood loss, HKA, FTC were better than the conventional group (P < 0.05). Conclusion Robot-assisted TKA has a short learning curve, abtains high accuracy, and can accurately reconstruct the lower limb alignment. It is less traumatic than conventional TKA, better clinical scores in the early postoperative period, but it also requires longer operation time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".