Iterative Design and Manufacturing of a Low-Cost, Low-Fidelity Simulator for Cricothyroidotomy
Bibliographic record
Abstract
In a trauma setting, cricothyroidotomy is a life-saving procedure performed to secure a compromised airway. The rarity and high-stakes nature of the procedure drastically limit training opportunities. Existing models are expensive and inaccessible. Our objective was to design a low-cost, low-fidelity cricothyroidotomy simulator using universally available materials to ensure widespread access. Our technical report used an iterative design-based research methodology. A multidisciplinary team first determined the essential steps of a cricothyroidotomy. We then established the anatomical components required to perform the procedure: skin, subcutaneous tissue, trachea, thyroid cartilage, and cricothyroid membrane. Multiple prototypes built from a variety of materials were tested by performing simulated cricothyroidotomies. Our cricothyroidotomy simulator requires less than five minutes to construct and costs $0.53 USD. The trachea is made from a plastic water bottle. An egg carton mimics the thyroid cartilage. A nitrile glove slides onto the trachea to simulate the cricothyroid membrane. Slime, created using a mixture of glue, detergent, sodium bicarbonate, and cornstarch, is used for the skin and subcutaneous tissues. Each component can be used infinitely, except for the nitrile glove, which needs to be replaced after 10 uses. Our cricothyroidotomy simulator can be used to train healthcare providers in both well-resourced and resource-limited settings. In addition to making our design blueprints open access, we have intentionally chosen widely accessible and eco-friendly materials to make our simulator available worldwide.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".