SURGICAL EFFICIENCY AND LEARNING CURVE DURING ROBOTIC-ASSISTED TOTAL KNEE ARTHROPLASTY
Bibliographic record
Abstract
Introduction Total knee arthroplasty (TKA) is a well established treatment option for patients with end stage osteoarthritis. Conventional TKA with manual instruments has been shown to be a cost effective and time efficient surgery. While robotic-assisted operative systems have been shown to have benefits in surgical accuracy, they have also been reported to have longer surgical times. The purpose of this work was to determine surgical time and learning curve for a novel robotic-assisted TKA platform. Methods Eighty-five subjects underwent robotic-assisted TKA by one of three investigators as part of an FDA and IRB approved Investigational Device Exemption (IDE). All patients received a cruciate retaining total knee implant system. Intra-operative safety, Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Knee Society Scores (KSS) were collected pre-operatively and at three month follow-up. In addition, surgical times were collected as part of a TKA work flow. To identify activities related to surgical steps required for robotic procedures specific time stamps were determined from the system. Capture of the hip center to final bone cut was used to define case time and identify robotic learning curve. Descriptive statistics were used to analyze results. Results Surgeon one completed 24 cases, surgeon two completed 32, and surgeon three completed 29 cases in the study. An average surgical time of 44 minutes with standard deviation of 15.7 minutes was recorded. On average surgeons improved in surgical time with increasing cases as indicated by linear regression results. During initial cases, surgeons repeated intra-operative planning steps which decreased with the learning curve. In addition, the average WOMAC score improvement from pre-operative to three months was 33.1 ± 20.04 (p Conclusion With cost related pressures in healthcare, hospitals and surgeons focus on improvements in surgical efficiency to stay competitive. The results of this study indicated comparable operative times to conventional TKA cases as reported in literature with the added benefit of optimizing surgical accuracy. Robotic solutions in TKA may become increasingly efficient as surgeons complete a learning curve.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".