Development and Evaluation of a 3D-Printed Adult Proximal Tibia Model for Simulation Training in Intraosseous Access
Bibliographic record
Abstract
Intraosseous infusion remains an underutilized technique for obtaining vascular access in adults, despite its potentially life-saving benefits in trauma patients and those presenting to the emergency department. There is a scarcity of cost-effective, anatomically correct trainers to improve physician confidence and competency in this skill. The purpose of this report is to describe the development and evaluation of a three-dimensional (3D) printed Adult Proximal Intraosseous (IO) Tibia task trainer for simulation-based medical education. The proposed trainer was designed by combining open-source models of a human skeleton and a lower leg surface scan in Blender (Blender Foundation - www.blender.org) and manipulating them further using a JavaScript program. Polylactic acid was used to simulate bone while cured silicone moulds were used to replicate skin and soft tissue. Two trainers were produced and tested by 15 rural family medicine residents, six rural emergency medicine physicians, and six registered nurses. Participants evaluated the realism of the trainer and its efficacy as a training tool through a structured survey. The trainer received overall positive feedback from all participants, and most participants felt that no improvements were required to use the trainer for medical education. Notable suggestions for improvement included adding an infusion component, increasing the size of the tibial tubercle for better landmarking, and creating a variety of sizes for different patient body types. Residents and emergency medicine physicians practising in rural Newfoundland and Labrador found the 3D-printed trainer to be a practical tool for practising intraosseous technique. The outcome of this report supports the use of this cost-effective trainer for simulation-based medical 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".