Physical reality simulation for training of laparoscopists in the 21st century. A multispecialty, multi-institutional study.
Bibliographic record
Abstract
BACKGROUND: Simulation is the most effective and safe way to train laparoscopic surgeons in an era of limited work hours, lack of funding, and increasing malpractice costs. However, the costs associated with the use of virtual reality simulators are significant, and although very technically sophisticated they still lack tactile feedback. We are proposing a physical reality simulator, the LTS 2000, as a reliable and effective alternative to virtual reality. This study was carried out to establish how reliably the simulator was able to differentiate between different levels of laparoscopic experience and to analyze the detection of skills improvement after simulation and clinical training. METHODS: This study was carried out, between July 2002 and August 2003, in the departments of Surgery and Obstetrics and Gynecology at 2 separate institutions. We enrolled 40 individuals in the study who had experience ranging from postgraduate year-1 to full-time faculty level. Five postgraduate year-3 residents were subsequently retested after rotating on clinical services, performing advanced laparoscopic procedures to assess whether the simulator was sensitive enough to detect improvements in laparoscopic skills at the intermediate level. Six tasks were included in the test, and they were scored for speed and precision with the McGill system. Two scores were obtained: a coordination score and a suturing score combined in a total score. Other variables analyzed were handedness, specialty, number of laparoscopic procedures performed, and hours spent on the simulator. RESULTS: Forty-five tests were performed. The number of subjects in each group based on level of experience was equally distributed. No difference occurred in scores between institutions, specialty, and right- or left-handed surgeons. A significant increase occurred in the coordination score and suturing score combined in the total score with increasing experience (P < 0.05) at each level. Furthermore, the simulator was sensitive enough to detect a significant difference in all 3 scores between subjects who had practiced with the simulator before being tested (P < 0.05). The scores of the 5 postgraduate year-3 participants doubled when tested, without reaching statistical significance due to the small sample size. CONCLUSIONS: Our study shows that the LTS 2000 reliably and reproducibly detects different levels of laparoscopic expertise and progression of the learning curve. LTS 2000 as a model of physical reality simulation should be considered a reliable alternative to virtual reality simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".