Low-cost three-dimensional printed phantom for neuraxial anesthesia training: Development and comparison to a commercial model
Bibliographic record
Abstract
Neuraxial anesthesia (spinal and epidural anesthesia) procedures have significant learning curves and have been traditionally taught at the bed side, exposing patients to the increased risk associated with procedures done by novices.Simulation based medical education allows trainees to repeatedly practice and hone their skills prior to patient interaction.Widespread adoption of simulation-based medical education for procedural teaching has been slow due to the expense and limited variety of commercially available phantoms.Free/Libre/ open-source (FLOS) software and desktop 3D printing technologies has enabled the fabrication of low-cost, patient-specific medical phantoms.However, few studies have evaluated the performance of these devices compared to commercially available phantoms.This paper describes the fabrication of a low-cost 3D printed neuraxial phantom based on computed tomorography (CT) scan data, and expert validation data comparing this phantom to a commercially available model. MethodsAnonymized CT DICOM data was segmented to create a 3D model of the lumbar spine.The 3D model was modified, placed inside a digitally designed housing unit and fabricated on a desktop 3D printer using polylactic acid (PLA) filament.The model was filled with an echogenic solution of gelatin with psyllium fiber.Twenty-two staff anesthesiologists performed a spinal and epidural on the 3D printed simulator and a commercially available Simulab phantom.Participants evaluated the tactile and ultrasound imaging fidelity of both phantoms via Likert-scale questionnaire.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".