Development and Evaluation of a Three-Dimensional-Printed Pediatric Intraosseous Infusion Simulator To Enhance Medical Training
Bibliographic record
Abstract
Vascular access is an essential and rate-limiting step during pediatric resuscitation efforts. Intraosseous (IO) access, an effective resuscitative strategy, remains underutilized in emergency departments. Many medical graduates report never performing the procedure before graduation, and it has been recommended that continuing education and in-servicing programs be implemented to increase the use and familiarity of IO access. The goal of this technical report is to describe the development and evaluation of a three-dimensional (3D)-printed Pediatric IO Infusion Model for simulation-based medical education. The simulator was designed by combining open-source models of a human skeleton and a lower leg surface scan in Blender (Blender Foundation, Amsterdam, Netherlands; www.blender.org), scaled to a pediatric size, and manipulated further using a JavaScript program. Polylactic acid was used to simulate bone while silicone molds were used as skin and soft tissue. Two trainers were produced and evaluated by seven emergency medicine physicians, two family medicine residents, and three medical students. Overall, the simulator was positively received with all participants indicating they would recommend it to assist in the training of others. Suggestions focused on enhancing the anatomical representations of both the skin and bones to enhance the learner experience. The content and outcomes of this report support the use of this simulator as part of simulation-based medical education.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".