Technical Protocol for Presenting Maxillofacial Prosthetics Concepts to Dental Students using Interactive 3D Virtual Models within a Portable Document Format
Bibliographic record
Abstract
An appropriate presentation of maxillofacial defects and their prosthetic rehabilitation concepts using traditional two-dimensional educational materials is challenging for dental students and prosthodontics residents. This technique article introduces a simple approach to visualize and communicate three-dimensional (3D) virtual models embedded into a portable document format (PDF) file for presenting maxillofacial prosthetics concepts and enhancing students' spatial ability when learning maxillofacial prosthetics. MeVisLab software was used to combine various maxillofacial models and save them as a single 3D model. Adobe Acrobat Pro DC software was used to import the 3D model and create interactive visualization PDF documents. Adobe reader software was then used to visualize the content of the PDF documents. This approach allows educators to develop PDF files with multiple 3D models for teaching maxillofacial prosthetics concepts and communicate them with their students. Students can simply open the PDF file, activate the 3D mode, and interactively manipulate the 3D models to enhance their spatial ability for learning maxillofacial prosthetics.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.254 | 0.078 |
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".