P.209 Novel 3D printing for complex cranial reconstruction in neurosurgery - A case series
Bibliographic record
Abstract
Background: Cranial reconstruction is a common consequence of neurosurgical disease and intervention following craniectomy, trauma, infection, congenital defects, or neoplasm. Most commonly, Computer Aided Design-Computer Aided Manufacturing (CAD-CAM) technology is utilized in cases where autologous bone cannot be used. These patient-specific implants provide excellent cosmesis, however individual costs range from $800-15,000, making routine use challenging in the current Canadian health care environment. Here we present a novel method using desktop 3D printers to manufacture patient-specific molds for intraoperative reconstruction of various cranial defects. Methods: Our first patient presented following two separate traumas requiring decompressive craniectomy and subsequent posterior fossa decompression without interval cranioplasty. The second patient required reconstruction during resection of intraosseous meningioma. Both cases were performed using a titanium-mesh/poly-methyl-methacrylate (Ti-PMMA) construct draped over the mold. The third case, cranioplasty following decompressive craniectomy outside the country, was performed using a “two piece” mold and PMMA was casted into the mold and allowed to harden. Results: Patients reported satisfaction with cosmesis, without adverse outcomes. Cost per case was $50-100, representing an estimated cost savings of $685,000 per year in Canada. Conclusions: Given excellent outcomes in addition to cost effectiveness, this case series provides evidence for use of this alternate technique with similar patient outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".