Bibliographic record
Abstract
Objective: The use of simulation for the advancement of laparoscopic skill among urology residency programs continues to advance.One purported benefit of simulation is that allows for objectification of technical skill, enabling the documentation of performance improvements as experience increases.The aim of this study was to demonstrate the construct validity of the instrument smoothness parameter in the ProMIS (Haptica Ltd., Dublin, Ireland) augmented reality simulator using the validated and standardized MISTELS laparoscopic tasks.Methods: Fifteen urology residents ranging from R5 to R1 were assessed using the ProMIS system on 3 occasions.The laparoscopic tasks included a peg transfer, intra and extra corporeal suturing, vessel looping and laparoscopic cutting.Smoothness of movement was measured by detecting the changes of instrument velocity over time (unitless) for each task.The values were recorded and subjected to statistical analysis using the students t test.Senior residents with standardized laparoscopic experience greater than 50 hours were compared to junior residents with less than 50 hours of cumulative experience. Results:The senior resident cohort demonstrated superior laparoscopic smoothness of movement in all 5 standardized laparoscopic tasks, demonstrating strong statistical significance (p < 0.05).This was further reflected in an improvement in overall task completion among the senior resident cohort as compared to the junior resident cohort.The senior resident group also demonstrated greater consistency of movement, as evidence by the standard deviations across tasks.This resulted in a 38% reduction in unnecessary laparoscopic instrument manipulation.Conclusions: These preliminary results of construct validity for the smoothness biometric parameter of the ProMIS simulator demonstrate its ability to distinguish between more experienced and novice urologic laparoscopists in a urology teaching program.This is a compelling feature of ProMIS that should facilitate its further incorporation into urology training programs worldwide.It further demonstrates that ProMIS can be used to assess, train and follow a variety of laparoscopic technical skills, and will enhance efficiency of laparoscopic movement, and possibly decreased operative time for patients.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.675 | 0.402 |
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".