Bibliographic record
Abstract
Introduction: Instrument motion tracking in surgical simulation is beneficial as a teaching and evaluation tool.recently, our group developed a synchronized motion-tracking simulation system for flexible ureteroscopy (furs).this study aimed to compare kinematic parameters between novices and experts during furs simulation.Methods: our system consisted of a 3d-printed kidney model within a urs simulation box.motion tracking sensors were attached to the scope's body and the distal tip of a ureteroscope.the body sensor tracked the surgeon's movement while the tip sensor tracked the location and intrarenal movement of the scope tip. a potentiometer was attached to the control lever to measure deflection, tracked as a percentage of maximum.the task was to map the kidney by traversing all its calyxes.Results: We recruited 10 participants, six pgY2s and two pgY3s (novices), and two endourology fellows (experts).the mean urs score was 10 for novices and 16 for experts (p=0.04).the mean path length for the intrarenal tip was 2182±613 mm for novices and 1164±290 mm for experts (p=0.03), while its mean speed was 12.2±2.1mm/sand 9.8±0.3mm/s respectively (p=0.01).Visualizing the tip path showed that novices traversed less renal area, especially for lateral calyxes (Figure 1a).Visualizing scope body movement showed that experts moved in a predictable wing-shaped pattern, while novices moved relatively randomly (Figure 1B). the average lever deflection magnitude was 5.7±2.4% for novices compared to 10.8±0.67% for experts (p=0.001).Conclusions: this is the first study that incorporated intrarenal tip tracking during furs motion analysis.our findings set the stage for adaptive and personalized learning, as novices have access to timely feedback and can understand the visual differences in tip path and scope body movement by comparing them to experts' results.our preliminary findings emphasize that experts show limited yet predictable scope body movement, greater lever deflection, and effective tip movement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.501 | 0.254 |
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".