Increasing Access to Medical Training With Three-Dimensional Printing: Creation of an Endotracheal Intubation Model (Preprint)
Bibliographic record
Abstract
BACKGROUND Endotracheal intubation (ETI) is a crucial life-saving procedure, where more than 2 failed attempts can lead to further complications or even death. Like all technical skills, ETI requires sufficient practice to perform adequately. Currently, the models used to practice ETI are expensive and, therefore, difficult to access, particularly in the developing world and in settings that lack a dedicated simulation center. OBJECTIVE This study aimed to improve access to ETI training by creating a comparable yet cost-effective simulation model producible by 3-dimensional (3D) printers. METHODS Open-source mesh files of relevant anatomy from BodyParts3D were modified through the 3D modeling programs Meshlab (ISTI-CNR) and Blender (Blender Foundation). Several prototypes with varying filaments were tried to optimize the ETI simulation. RESULTS We have created the novel 3D-printed pediatric ETI model for learners at all levels to practice this airway management skill at negligible costs compared with current simulation models. It is an open-source design available for all medical trainees. CONCLUSIONS Revolutions in cost and ease of use have allowed home and even desktop 3D printers to become widespread. Therefore, open-source access to the ETI model will improve accessibility to medical training in the hopes of optimizing patient care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".