Cost-Effective Cranioplasty Utilizing 3D Printed Molds: A Canadian Single-Center Experience
Bibliographic record
Abstract
BACKGROUND: Cranioplasty is a commonly performed neurosurgical procedure used to repair defects of the cranial vault. For large defects, 3D printing allows for the creation of patient-specific synthetic cranioplasties. Although these implants provide excellent cosmetic results for patients, costs are quite high. This makes their routine use challenging in the current Canadian healthcare environment. The purpose of this study is to report our experience with a novel, cost-effective method for cranioplasty using desktop 3D printers to manufacture patient-specific molds to aid in the shaping of polymethyl methacrylate (PMMA) cranioplasty intraoperatively. METHODS: A retrospective review of patients who underwent cranioplasty utilizing 3D printed custom molds was conducted at a single center between 2018 and 2020. Either a two-piece self-align or open-air mold was utilized. Material cost, as well as demographic, clinical, and radiologic data, was reviewed. A five-point ordinance scale was used to evaluate patient satisfaction with cosmesis. RESULTS: Four patients had previous craniectomies with infected bone flaps, 2 patients had significant bony destruction from tumor invasion, and 1 patient had bone flap resorption. Three patients underwent an open-air mold technique with a Ti-mesh/PMMA-combined implant. The remaining 4 patients underwent two-piece mold with PMMA-only implant. All patients had 'Good' to 'Excellent' cosmetic outcome with one post-operative acute subdural hematoma and one post-operative infection. Two-piece mold resulted in improved cosmetic outcome and cost savings. CONCLUSIONS: 3D printing can be used in a cost-effective manner to deliver good cranioplasty cosmesis. Wider adoption of this technique can result in significant healthcare cost savings without compromising patient outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".