Ultrasonic tool to cut human bone: Cutting speed and forces necessary for potential remote robotic arms
Bibliographic record
Abstract
Procedural-related musculoskeletal pain is common among orthopaedic surgeons, often caused by the repetitive use of high-force bone-cutting tools. Ultrasonic cutting devices, which can operate with lower force, may help reduce this physical burden. In this study, three practising orthopaedic surgeons each performed two cuts on three fresh cortical bone samples, harvested from excised femoral necks from three patients undergoing hip replacement surgery. The study was conducted using an ultrasonic cutting device in a controlled yet clinically reflective environment. A novel setup captured real-time data on surgeon-related parameters, including vertical cutting force and vertical and horizontal cutting speed. Consistent with previous research, we confirmed that ultrasonic devices enable low force cutting (average 1.91 N). However, our findings revealed significant variability in how each surgeon interacted with the device - including how much force each surgeon applied, and how the device was manoeuvred which can influence device performance, thermal effects, and overall clinical outcomes. Given the critical importance of surgeon-related factors, our results highlight the need to understand how each surgeon interacts with these devices differently. This insight can inform training and device optimisation strategies; help translate bench testing results into effective clinical use and ultimately improve surgical performance and patient outcomes. Additionally, our findings support the potential benefits of integrating ultrasonic devices with robotic platforms to maintain consistent cutting parameters. Future research should investigate optimal cutting parameters, evaluate different blade profiles, assess result generalisability and compare outcomes before and after training or system enhancements.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".