Development of a Three-dimensional Printed Emergent Burr Hole and Craniotomy Simulator
Bibliographic record
Abstract
Patients with a traumatic head injury (THI) require immediate surgical intervention, as rapidly expanding intracranial hematomas can be imminently life-threatening, not permitting transfer time to neurosurgical care in a tertiary care center. In rural and remote areas, where neurosurgeons may not be readily available, surgical intervention by Community General Surgeons (CGS) may be required. Currently, the CGS in Newfoundland and Labrador (NL) do not have access to, or have experience training with, an emergent burr hole/craniotomy (EBHC) simulator. One of the barriers is the availability of inexpensive and reliable simulators to practice these skills. Therefore, a low-cost, three-dimensional (3D) printed EBHC simulator was designed and 3D-printed by MUN Med 3D (St John's, NL). The aim of this technical report is to assess the need for such simulator in rural and remote healthcare centers and report on the iterative development of the EBHC simulator. The 3D-printed EBHC simulator developed by MUN Med 3D was utilized during a general surgery workshop at the 26th Annual Rural and Remote Medicine Conference in St. John’s, NL. A total of six 3D-printed EBHC simulators were provided for the hour and a half workshop. At the end of the workshop, 16 participants were asked to provide feedback on the need for this simulator in their rural or remote environment as well as feedback on the physical attributes. The feedback received from the participants was overall positive, informative, and supported the need for the 3D-printed EBHC simulator.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".