In-Theatre Simulation as a Training Tool for Laparoscopic Salpingectomy in Eldoret, Kenya
Bibliographic record
Abstract
Background/Objective: Minimally invasive surgery (MIS) offers many advantages over open procedures including decreased patient safety risks and reduced burden on healthcare infrastructure. As low- and middle-income countries (LMICs) are disproportionately affected by these aspects of surgery, there is motivation to increase MIS. A multimodal training program in laparoscopic salpingectomies was piloted with a small cohort of OB-GYN registrars and consultants at Moi Teaching and Referral Hospital (MTRH) in Eldoret, Kenya. This project assesses the in-theatre simulation’s (1) effectiveness in improving laparoscopic knowledge and skill confidence, and (2) feasibility for long-term implementation at MTRH and in similar settings. Methods: Participants completed a half-day in-theatre simulation of a laparoscopic salpingectomy. The simulation required participants to demonstrate knowledge of laparoscopic setup, proper patient positioning, procedure completion, equipment troubleshooting, and peri-and intra-operative complication management. Participants completed a multiple-choice laparoscopic knowledge quiz and Likert scale skill confidence survey immediately prior to and following the simulation. Pre- and post-simulation responses were compared to assess knowledge and confidence acquisition overall and across content topics. Results: There was a significant increase in the average knowledge quiz score from pre- to posttest (p=0.028). A significant difference between pre- and posttest confidence was noted in four of the six skills assessed. By topic, equipment troubleshooting (p<0.001), and complication management (p<0.01) saw the most improvement. Barriers to long-term sustainability include unpredictable theatre and laparoscopic tower access and availability of supplies for uterine modeling. A modified model using nitrile gloves as fallopian tubes will be piloted in future simulations as a more accessible alternative for long-term implementation. Conclusion: Despite limitations, in-theatre simulation has the potential to be an effective and sustainable teaching tool within a long-term MIS training program at Moi Teaching and Referral Hospital. The low-cost model and methods outlined may also be replicable in similar low-resource settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".