Development of an Open-Source Laparoscopic Simulator Capable of Motion and Force Assessment: High Tech at Low Cost
Bibliographic record
Abstract
OBJECTIVE: Laparoscopic simulators help improve surgical skills in an ex vivo setting. New simulators incorporate force and motion assessment, but often at high financial cost. Our goal is to establish global access to a laparoscopic simulator, which offers both traditional summative assessment (time to task completion and precision) as well as advanced formative assessment (force and motion sensing capabilities) so that educators anywhere may be able to create simulators with increased educational value. DESIGN: A low-cost laparoscopic simulator incorporating an off-the-shelf optical sensor, inertial measurement unit, holders, and a housing unit for a microcontroller was integrated into a plastic box with a high-definition digital camera and a three-dimensional mouse. Open source software was developed to offer real-time feedback in force and motion. The system was calibrated for accuracy and consistency. RESULTS: -values of between 0.988 and 0.999. Accuracy in motion evaluation was evaluated and this demonstrated low average errors in the motion sensors of 5.4% to 6.8%. CONCLUSIONS: This low-cost, off-the-shelf, open-access laparoscopic simulator provides accurate and consistent measures of force and motion. We believe that collaborative efforts between surgeons and engineers can allow the creation of these surgical teaching devices at a reasonable cost such that they can be used in resource-rich and resource-limited settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".