The learning curve of a novel seven-axis robot-assisted total hip arthroplasty system: a randomized controlled trial
Bibliographic record
Abstract
BACGROUND: The aim of this study was to assess the learning curve of a novel seven-axis robot-assisted total hip arthroplasty (RaTHA) system. METHODS: A total of 59 patients who underwent unilateral total hip arthroplasty at our institution from June 2022 to September 2022 were prospectively included in the study. In this randomized controlled clinical trial, robot-assisted THA (RaTHA) and Conventional THA (CoTHA) were performed using cumulative sum (CUSUM) analysis to evaluate the learning curve of the RaTHA system. The demographic data, preopera1tive clinical data, duration of operation, postoperative Harris Hip Score (HHS), postoperative Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and duration of operation between the learning stage and the proficiency stage of the RaTHA group were compared between the two groups. RESULTS: The average duration of operation of the RaTHA group was increased by 34.73 min compared with the CoTHA group (104.26 ± 19.33 vs. 69.53 ± 18.38 min, p < 0.01). The learning curve of the RaTHA system can be divided into learning stage and proficiency stage, and the former consists of the first 13 cases by CUSUM analysis. In the RaTHA group, the duration of operation decreased by 29.75 min in the proficiency stage compared to the learning stage (121.12 ± 12.84 vs.91.37 ± 12.92, p < 0.01). CONCLUSIONS: This study demonstrated that the surgical team required a learning curve of 13 cases to become proficient using the RaTHA system. The duration of operation, total blood loss, and drainage gradually shortened (decreased) with the learning curve stage, and the differences were statistically significant. TRIAL REGISTRATION: Number: ChiCTR2200061630, Date: 29/06/2022.
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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 | 0.005 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".