Application of 3D Printing Support Material for Neurosurgical Simulation
Bibliographic record
Abstract
Brain dissection, an intricate neurosurgical skill, is central to life-saving procedures such as intrinsic brain tumor excision and resective epilepsy surgery. The aims of this manuscript are to outline the selection process of a suitable material for the development of a dissectible brain simulator and to present the use of support material, SUP 706, manufactured by Stratasys Ltd. as a non-waste alternative for sustainably engineering solutions for surgical education. A feasibility study was conducted through qualitative function deployment (QFD) followed by a material selection process. End-user requirements and manufacturing product characteristics were incorporated into the workflow. Three materials, silicone, TissueMatrix™ and support material each formed the primary component of the first two prototypes. Expert feedback, manufacturing cost, safety profiling, functional fidelity and post-processing time data were collected and analyzed. The unique break-away feature of moist support material was found to be more suitable than using silicone or TissueMatrix™ for demonstrating brain dissection techniques. In addition, support material displayed higher functional fidelity by mimicking surgical tissues such as pia mater, gray and white matter, and blood vessels. The cost of the support material prototype was 39% less that of TissueMatrix™ and roughly the same as the silicone model. It took twice as long to post-process the support material prototype than it did the TissueMatrix™ design. Support material lost its ideal dissection properties and began to disintegrate after 30 - 45 minutes. In conclusion 3D printer support material is a low-cost material for a dissectible brain simulator.Clinical Relevance- The use of support material as the primary material in developing a dissectible brain simulator is a promising way of advancing neurosurgical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".