Development and evaluation of a sensorized shoulder simulator
Bibliographic record
Abstract
The need for skilled arthroscopic surgeons is increasing due to the large number of arthroscopic interventions performed annually. Surgical simulators are beneficial training platforms for practicing those difficult to learn surgical tasks. In this study, a sensorized physical shoulder simulator was developed. This simulator incorporates switch sensors for objective assessment of probing tasks and a force sensor for measuring applied forces at the simulator base. In addition, arthroscopic instruments were sensorized with force and position sensors to measure the forces applied at the tip of the instruments and track the position of the tip of these instrument. Face and construct validity of the simulator was assessed by conducting an experiment involving expert and novice subjects. The results show that experts were 90.8% satisfied with the quality and benefits of the simulator. In addition, statistically significant differences were found between experts and novices in seven out of nine metrics investigated here, which supports construct validity of the simulator. Use of the sensorized simulator allows independent training and objective assessment of skill levels, and can enhance surgical training and skills assessment.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".