Use of 3-Dimensional Printing to Create Patient-Specific Thoracic Spine Models as Task Trainers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Thoracic epidural anesthesia is a technically challenging procedure with a high failure rate of 24% to 32% nationwide. Residents in anesthesiology have limited opportunities to practice this technique adequately, and there are no training tools available for this purpose. Our objective was to build a low-cost patient-specific thoracic epidural training model. METHODS: We obtained thoracic computed tomography scan data from patients with normal and kyphotic spine. The thoracic spine was segmented from the scan, and a 3-dimensional model of the spine was generated and printed. It was then placed in a customized wooden box and filled with different types of silicone to mimic human tissues. Attending physicians in our institution then tested the final model. They were asked to fill out a brief questionnaire after the identification of the landmarks and epidural space using ultrasound and real-time performance for a thoracic epidural on the model (Supplemental Digital Content 1, http://links.lww.com/AAP/A197). Likert scoring system was used for scoring. RESULTS: The time to develop this simulator model took less than 4 days, and the materials cost approximately $400. Fourteen physicians tested the model for determining the realistic sensation while palpating the spinous process, needle entry through the silicone, the "pop" sensation and ultrasound fidelity of the model. Whereas the tactile fidelity scores were "neutral" (3.08, 3.06, and 3.0, respectively), the ultrasound guidance and overall suitability for residents were highly rated as being the most realistic (4.85 and 4.0, respectively). CONCLUSIONS: It is possible to develop homemade, low-cost, patient-specific, and high-fidelity ultrasound guidance simulators for resident training in thoracic epidurals using 3-dimensional printing technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".