INTRA-OPERATIVE SAFETY AND EARLY PATIENT RECOVERY IN ROBOTIC ARM ASSISTED TOTAL KNEE ARTHROPLASTY
Bibliographic record
Abstract
Introduction Total knee arthroplasty (TKA) using conventional instrumentation has been shown to be a safe and effective way of treating end stage osteoarthritis by restoring function and alleviating pain. As robotic technology is developed to assist surgeons with intra-operative decision making such as joint balancing and component positioning, the safety of these advancements must be established. Furthermore, functional recovery and clinical outcomes should achieve comparable results to the gold standard of conventional instrumentation TKA. Methods Eighty-seven subjects (89 knees) underwent robotic arm assisted TKA by one of three investigators as part of an FDA and IRB approved Investigational Device Exemption (IDE). To achieve the primary endpoint of intra-operative patient safety using a robotic arm assisted cutting tool, the investigators completed questionnaires to assess a series of complications related to soft tissue damage associated with conventional TKA. Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Knee Society Knee Scores (KSS) were collected pre-operatively and at three month follow-up. Results The average subject age was 65.8 ± 8.5 and the average BMI was 31.4 ± 5.7. 55% of the subjects were female. No subject experienced any of the rare intra-operative complications that comprise the primary safety endpoint. The average WOMAC score improvement from pre-operative to three months was 33.1 ± 20.04 (p Conclusion Results from this prospective robotic arm assisted trial indicated greater intra-operative safety in blood loss and ligamentous damage when compared to conventional TKA. Subjects had excellent return to function and pain relief by three months as indicated by the KSS and WOMAC scores. Robotic technologies should be considered a safe and effective method in total joint restoration.
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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 | 0.002 | 0.003 |
| 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) | 0.001 | 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".