The Development and Validation of CranioFix: An Augmented-Reality Biomodel for Craniomaxillofacial Education
Bibliographic record
Abstract
OBJECTIVE: To describe the development, implementation, and validation of CranioFix, an augmented-reality (AR)-enhanced educational toolkit for the training of craniomaxillofacial trauma management, specifically orbit-zygomatic complex (OZC) fractures. BACKGROUND: Surgical training in craniomaxillofacial trauma presents significant challenges due to the complexity of the anatomy, limited exposure to trauma cases, and the need for hands-on technical skill acquisition. Augmented-reality simulation technologies offer a promising solution to these limitations. METHODS: CranioFix was developed as a hybrid AR and physical simulation model consisting of a 3D-printed skull with realistic tissue overlays, integrated surgical instruments, and a smartphone-based AR application. The app provides guided surgical instruction, anatomic overlays, and self-assessment tools. A validation study involving 15 surgeons and surgical trainees was conducted in a war-zone mission setting. RESULTS: Participants reported significant improvement in confidence levels for OZC trauma management (mean precourse 3.2/10 versus postcourse 6.3/10). The educational content received high ratings for usefulness (9.1/10), interactivity (8.3/10), and overall design (8.2/10). All participants endorsed CranioFix as a valuable training tool and recommended its expansion to other craniofacial procedures. CONCLUSION: CranioFix is an effective, portable, and scalable AR-based simulation platform that enhances surgical training in craniomaxillofacial trauma. It is particularly valuable in resource-constrained or disrupted settings and has the potential for wide application in global surgical education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".