Hacking Intraosseous Infusion Skills Training With 3D Printing: maxSIMIO Drilling System
Bibliographic record
Abstract
Intraosseous (IO) infusion is an alternative way to access the vascular system to administer drugs and fluids, which is particularly helpful when the commonly used peripheral intravenous route is inaccessible. The IO procedure can be done using a drill that involves disinfecting the area, landmarking the insertion point, seating the needle in a firm and stable position in the bone, and then delivering a smooth fluid flush. However, in the current medical training landscape, access to commercially available IO drills such as the Arrow® EZ-IO® Power Driver (EZ-IO; Teleflex, Morrisville, North Carolina, United States) is difficult, especially for rural and remote areas, due to the high costs. Furthermore, the EZ-IO is not rechargeable and does not clearly indicate the remaining battery life, which could potentially put patients at risk during the IO procedure. This technical report aims to address these concerns by describing the development of an alternative, affordable, and reliable IO drilling system for training use: the maxSIMIO Drilling System. This system consists of a cordless and rechargeable IKEA screwdriver which connects to a conventional, hexagon-shaped 3D-printed drill bit needle adapter. Two needle adapters were created: Version A was designed to use a friction-based mechanism to couple the screwdriver with the EZ-IO training needle, while Version B relies on a magnetic mechanism. The major differences between the EZ-IO and the screwdriver are that a) the EZ-IO has only one rotation to advance the cannula while the screwdriver features both directions, b) the EZ-IO is not rechargeable while the screwdriver is, and c) the EZ-IO has a custom needle holder that can fit any EZ-IO training needle size while the screwdriver needs to have a custom needle adapter made to connect to the EZ-IO training needle. Overall, through this exploration, the features of the maxSIMIO Drilling System in comparison to the EZ-IO appear more accessible for IO training. Future considerations for this development include gathering clinical expertise through rigorous testing of this novel system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".