Development of In-situ Simulation Lab for Training Gynecology Residents in Basic Laparoscopic and Hysteroscopic Operative Skills
Bibliographic record
Abstract
INTRODUCTION: Mounting evidence suggests that practice on simulators leads to improved operative skills and patient safety. With restrictions on resident work hours resulting in less exposure to procedures, simulation is the key to developing operative skills during residency and beyond. Residency programs struggle with implementing a simulation program due to timing and availability of residents. Despite having a large centralized simulation space at our institution, we identified lack of dedicated gynecologic simulation curriculum and simulator accessibility as our greatest barriers to utilizing simulation training in gynecology resident education. We sought to design a space within the resident work area dedicated to gynecologic simulation training with specific curriculum and objectives for each work station based on residency year level. METHODS: We created four workstations in a room within the Ob/Gyn clinic, in close proximity to the resident offices. Two virtual reality simulators, the LapVR (CAE, Montreal, Canada) ($84,996.00) and Simbionix Hystsim (3D Systems, (formerly Simbionix), CO, USA) ($95,741.10), were acquired from our institution's simulation center and placed in this training space to allow for enhanced resident access. The two other work stations consisted of an FLS trainer box and monitor ($1580) and another low fidelity laparoscopic box trainer and monitor ($450). Specific objectives for each station with corresponding evaluation checklists were written for each residency year level. Dedicated time to meet the written objectives was given to residents each week during their benign gynecology rotation. Supervision and assistance with task completion was provided by staff mentors assigned during those shifts. RESULTS: Residents who had this simulation lab available to them during their gynecology rotation participated in a minimum of seven hours of simulation time in addition to the time they spent on their own in the lab. These residents felt this was a meaningful increase in the amount of laparoscopic and hysteroscopic simulation exposure by having access to this in-situ GYN Simulation Training Laboratory with a defined gynecologic curriculum. Multiple staff members also took advantage of the simulation lab to practice their skills as well. CONCLUSIONS: We created an in-situ Gyn Simulation Training Lab that allowed for both improved accessibility by the residents and ease of implementation of simulation curriculum into pre-existing resident didactic time. It is our opinion that the time residents spend engaged in surgical simulation will improve surgical skills and confidence thereby enhancing patient safety. Additionally, the creation of this in situ simulation lab assists in meeting the Accreditation Council for Graduate Medical Education (ACGME) requirements for incorporation of simulation into OB/GYN resident education.
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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 | 0.000 | 0.000 |
| 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".