[Comparison of clinical efficacy between robotic-assisted total hip arthroplasty and traditional total hip arthroplasty].
Bibliographic record
Abstract
OBJECTIVE: To explore and analyze the clinical efficacy of robotic-assisted versus traditional total hip arthroplasty. METHODS: ;13 cases involved the left hip, and 22 cases involved the right hip. The following parameters were analyzed and compared between the two groups:acetabular anteversion angle, acetabular abduction angle, difference in combined offset, difference in lower limb length, proportion of acetabula located in the Lewinnek safe zone after surgery, operation time, visual analogue scale (VAS) score, Western Ontario and McMaster Universities osteoarthritis index (WOMAC) score, and Harris hip score (HHS). RESULTS: <0.05):the differences in lower limb length were (3.17±0.15) mm and (5.28±0.47) mm respectively;the postoperative acetabular anteversion angles were(22.84±2.83)° and (25.72±3.29)° respectively;the HHS were (80.7±5.5) and (74.8±6.3) respectively;and the operation times were (148.20±46.82) minutes and (81.84±18.76) minutes respectively. CONCLUSION: Robot-assisted total hip arthroplasty demonstrates superior implant accuracy and improved early functional recovery compared with traditional manual THA. Nevertheless, it is associated with significantly longer operation time. Long-term prosthesis survival rate requires further follow-up verification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".