Bibliographic record
Abstract
Introduction: Hand/instrument motion-tracking in surgical simulation provides valuable data to improve psychomotor skills and can serve as a formative evaluation tool.While motion analysis has been well-studied in laparoscopic surgery, it has been poorly studied in endoscopic surgery.Very few studies look at motion-tracking for flexible ureteroscopy (fURS), a common surgical procedure that requires hand dexterity and 3D spatial awareness.To address this gap, we designed a synchronized motion-tracking and video capture system for fURS capable of collecting objective metrics for use in surgical skills training.Methods: A single-use flexible ureteroscope was used to design and test our system.Motion tracking of the ureteroscope was performed using the Polhemus Patriot platform, inertial measurement units (IMUs), and an optical sensor.Specifically, the position (x, y, z) and orientation (roll, pitch, yaw) of the ureteroscope handle, deflection of the ureteroscope lever, and translation of the scope insertion point were collected.Video capture of the operator's hands was collected with a Raspberry Pi camera, and the recording of the endoscopic view was collected from the video tower.All peripherals were controlled by a Raspberry Pi 4 and synchronized to its system clock.Results: Our system demonstrated good accuracy in detecting translation of the ureteroscope in the x-and y-axes, and yaw, pitch, and roll of the ureteroscope at discrete orientations of 0, ±30, ±60, and ±90 degrees.Unique to fURS, the deflection of the lever was captured by the difference in IMU static accelerations with good accuracy.The optical sensor detected the translation of the ureteroscope at the insertion point with an average error of 5.51% when traversing distances of 25, 50, and 100 mm a total of 10 times each.Conclusions: We successfully developed a system capable of collecting motionanalysis parameters and capturing videos unique to fURS.Future studies will focus on establishing the construct validity of this tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.497 | 0.317 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".