Advancing Surgical Precision in Z-Plasty and Melanoma Excision Through Quality Improvement Initiatives in Rural Settings
Bibliographic record
Abstract
Rural healthcare provider shortages have a severe impact on Canadians who seek care in rural and remote (R&R) settings, often arriving with poor health or medical emergencies. Healthcare providers working in such settings often face significant challenges in accessing skills development and maintenance courses to meet the unique medical demands of rural communities. As a result, it is vital to provide R&R healthcare providers with the appropriate simulation-based skills training. This approach led to the development of a Z-plasty and melanoma simulator tool, which was presented at a workshop during the Society of Rural Physicians of Canada (SRPC) conference in Niagara Falls, Ontario, from April 20 to 22, 2023. The workshop aimed to familiarize participants with the procedures and instruments required for Z-plasty and melanoma excisions in R&R practice. This paper describes the development of the simulators used in the foundational skills workshop, attended by medical students, residents, and physicians. It also analyzes the workshop's findings to guide future enhancements. The Z-plasty and melanoma simulators were created using additive manufacturing techniques, including three-dimensional printing and silicone. Participants in the SRPC Rural and Remote Medicine Course evaluated the functionality and realism of the simulators and provided feedback for improvements, using the Michigan Standard Simulation Experience Scale. Quantitative data indicated that the Z-plasty simulator achieved an overall score of 4.03 on a 5-point Likert scale, while the melanoma simulator scored 4.15. Participants' feedback was categorized into three main areas: self-efficacy, realism, and educational value. Qualitative analysis of the data revealed three themes for the Z-plasty simulator: physical resilience, materials science, and skills development. Similarly, the melanoma simulator yielded two main themes: physical reliance and materials science. Overall, the simulators demonstrated effective hands-on practice, representing a sustainable method for developing skills-based competencies in Z-plasty and melanoma excisions for R&R settings. Feedback from workshop participants will inform ongoing improvements to the simulators and their integration into future training events.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".