[Short-term effectiveness of novel computer navigation system assisted total knee arthroplasty].
Bibliographic record
Abstract
OBJECTIVE: To investigate the short-term effectiveness of novel computer navigation system (Knee 3 software; Brainlab, Germany) assisted total knee arthroplasty (TKA). METHODS: Between July 2020 and December 2020, 19 patients underwent unilateral TKA assisted with Knee 3 software. There were 4 males and 15 females. The mean age was 66.3 years (range, 52-79 years). Eighteen patients were diagnosed with osteoarthritis and 1 patient with rheumatoid arthritis. Sixteen patients had varus knees and 3 patients had valgus knees. Preoperative Western Ontario and McMaster University Osteoarthritis Index (WOMAC) pain, stiffness, function, and total scores were 12.4±3.4, 2 (1, 4), 22 (18, 29), and 37 (29, 43), respectively. Intraoperatively, the medial and lateral gaps in knee extension and in 90° of knee flexion were recorded. The operation time, intraoperative blood loss, blood transfusion, and complications were recorded. The hip-knee-ankle angle (HKA), lateral distal femoral angle, and medial proximal tibial angle were measured to evaluate lower limb alignment and prostheses's alignment using X-ray films at 6 weeks after operation. Patient's satisfaction rate and WOMAC pain, stiffness, function, and total scores were investigated. RESULTS: <0.05). Twelve patients were very satisfied with the operation results and 7 patients were satisfied with the operation results. The overall satisfaction rate was 100%. CONCLUSION: Knee 3 software can help to obtain good gap balancing and optimal lower limb alignment, with high patient's satisfaction and good short-term effecectiveness.
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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 | 0.000 | 0.001 |
| 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".