Performance Evaluation of a Sensorized Arthroscopic Grasper
Bibliographic record
Abstract
Force sensing minimally invasive instruments have gained increasing attention in recent years. Integrating these instruments within currently available surgical simulators can enhance the learning experience by measuring the forces applied by trainees and supplementing objective performance assessment. Recently, an arthroscopic grasper was designed and sensorized with Fiber Bragg Grating Sensors at Canadian Surgical Technologies and Advanced Robotics (CSTAR). Moreover, a custom low-cost (LC) interrogation system was developed to accompany the proposed sensorized tool. In this study, the custom LC interrogator was used and compared to the commercially-available Micron Optics sm130 (MO) interrogator. The hypothesis is that both of these systems can be used to measure forces within ±0.5 N as the acceptable margin for accuracy. Experimental results showed that the MO system meets the required accuracy for certain force directions. The LC system demonstrated 49% of the accuracy of the MO interrogator. The main advantage of the LC interrogator is its cost, which is 18% of the commercial interrogation system. For certain force directions, the performance was comparable to the defined criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".