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 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 | 0.002 | 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 | 0.001 |
| 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".