Print-It-Yourself Bone Marrow Biopsy Simulator Compared With Human Cadavers
Bibliographic record
Abstract
INTRODUCTION: Bone marrow biopsy is a critical skill in hematology-oncology and internal medicine training. Less experienced practitioners more frequently obtain suboptimal specimens, leading to misdiagnoses, delays, or repeated procedures; rare but serious complications can occur during training on actual patients. Although cadaveric simulation and commercial simulators are valuable training tools, they present significant cost and accessibility challenges. Current 3-dimensional (3D)-printed simulators lack essential features necessary for widespread implementation. METHODS: Based on the biomedical properties of the posterior superior iliac spine, we designed a 3D-printed procedural simulator to democratize bone marrow biopsy training. Key features include functional fidelity to enhance learner confidence and rapid acquisition of high-quality specimens (at least 15 mm in length), ease of printing and assembly, low cost, and durability. We compared this simulator to cadaveric practice in a randomized crossover study. RESULTS: Thirty-eight first-year internal medicine residents participated in the simulation. Residents reported similar levels of appreciation and confidence in performing a biopsy after practicing on the novel 3D-printed simulators and human cadavers. Within 10 minutes of practice with the simulator, 86% successfully obtained lifelike specimens of at least 15 mm (2.5 specimens, mean 27 mm), compared to 60% with cadaver practice (2.1 specimens, mean 17 mm). CONCLUSIONS: Using this 3D-printed simulator, residents obtained higher quality specimens than with cadaveric practice, allowing them to focus on mastering the controlled drilling motion through the bone cortex and marrow, the key challenge of this procedure. Democratizing biopsy simulation, educators can easily assemble this low-cost simulator for use in diverse training settings.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".