Development and Implementation of a Three-dimensional Printed Knee Joint Simulation Model Using the Consolidated Framework for Implementation Research: Addressing Local Simulation Needs
Bibliographic record
Abstract
Background Knee joint injections and aspirations are essential procedures for medical students, residents, and primary care physicians to master. Simulation-based training has been shown to improve learner confidence and performance scores in knee joint injections. Current knee joint simulators are expensive, ranging from hundreds to thousands of dollars. Using three-dimensional (3D) printing and gel layering technology, we designed and manufactured an inexpensive simulator. The aim of this implementation study was to gather the opinions of local simulation specialists and administrators regarding the simulator's curricular implementation. Methods Using the Consolidated Framework for Implementation Research (CFIR), we developed a 31-item implementation survey. It was administered to local simulation specialists and administrators. The purpose of the survey was to identify the aspects of the simulator that they deemed important to the implementation process, as well as obtain their qualitative feedback about the design. Results In total, three participants completed the survey. There were 16 survey items that were rated as very important, including local manufacturing, appropriate planning, internal development, evidence-based development, and reasonable costs. Another nine items were deemed important, including the adaptability of the product and ability to test the product. The simulation specialists also expressed some concerns they had with the design of the simulator and made suggestions about how we could address these concerns. Conclusions Local development and manufacturing, coupled with appropriate pre-implementation planning and efficacy evidence, were selected as factors that would potentially contribute to the success of the implementation of the simulator in the local curriculum.
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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 | 0.189 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".