Cricothyroidotomy Simulator: A Makerspace and Augmented Reality Approach
Bibliographic record
Abstract
A cricothyroidotomy is an incision made on the cricothyroid membrane at its midline to create an airway for oxygenation and ventilation. Cricothyroidotomy is a complex procedure involving motor, psychological, and decision-making skills, amply used during the COVID-19 pandemic. This procedure requires extensive training, and simulators facilitate teaching advanced airway management techniques to health care professionals. However, upper airway simulators are expensive and limited to specialized facilities inaccessible during the COVID-19 pandemic when in-laboratory practices shifted to online synchronous and asynchronous teaching. Such a scenario sparked interest in makerspace technologies for creating cost-effective simulators. This paper presents the prototyping of a cricothyroidotomy simulator through a Design Thinking approach to ideate a cost-effective solution that contains all 3D printed structures properly representing the real anatomical parts needed for the procedure. Additionally, we propose an augmentation of the 3D printed model employing Augmented Reality (AR) to enhance how information about the procedure can be accessed without relying on traditional instruction materials. Our preliminary results have led to a makerspace cricothyrotomy simulator used in training sessions in conferences and workshops and the prototyping of an AR complementary tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".