Computer‐Assisted Total Hip Arthroplasty Improves Acetabular Prosthesis Placement Accuracy: A Multicenter, Randomized Controlled Clinical Study
Bibliographic record
Abstract
OBJECTIVE: The long-term effectiveness of total hip arthroplasty (THA) largely depends on the accuracy of acetabular prosthesis placement. To improve the accuracy of acetabular prosthesis placement, we utilized a new surgical navigation system: visual treatment solution (VTS). The purpose of this study was to verify the efficacy and safety of this system in assisting THA. METHODS: This was a prospective, multicenter, randomized controlled trial. One hundred and twenty-four patients undergoing primary THAs were included. The experimental group underwent VTS-assisted THA, and the control group underwent traditional surgical techniques. The main efficacy evaluation indicators were the proportion of anteversion and inclination angles in the Lewinnek safe zone, and secondary evaluation indicators included operation time, Western Ontario and McMaster University Osteoarthritis index (WOMAC) score, Harris score, short-form-36 (SF-36) score, and hip dislocation rate. Statistical analysis was performed mainly by t-test and chi-square test. RESULTS: The proportion of both anteversion and inclination angles in the safe zone was 93.1% in the experimental group and 50.9% in the control group; the difference was significant (p < 0.01). The average operation time was 112.5 min in the experimental group and 92.6 min in the control group; the difference was significant (p < 0.01). There were no significant differences in WOMAC score, Harris score, or SF-36 score between the experimental and control groups at 3 months after the operation (p > 0.05). The dislocation rate was 0% in the experimental group and 1.6% in the control group; the difference was not significant (p > 0.05). CONCLUSION: VTS-assisted THA can significantly improve the accuracy of acetabular prosthesis placement. However, there were no differences in short-term clinical outcomes or dislocation rates between the two groups.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".