Generation of a 3D Printed Temporal Bone Model with Internal Fidelity and Validation of the Mechanical Construct
Bibliographic record
Abstract
OBJECTIVE: To generate a rapid-prototyped temporal bone model from computed tomography (CT) data with a specific focus on internal anatomic fidelity. STUDY DESIGN: Research ethics board-approved prospective cohort study. SETTING: Current iterations of a rapid-prototyped temporal bone model are complicated by absent void spaces and inconsistent bone density due to limited infiltrant exposure. The creation of a high-fidelity model allows surgical trainees to practice in a standardized and reproducible training environment. This learning paradigm will significantly augment resident understanding of surgical approaches and techniques to prevent adverse outcomes. SUBJECTS AND METHODS: We describe a technique for generating internally accurate rapid-prototyped anatomical models with solid and hollow structures, including void spaces. The novel slicing algorithm digitally deconstructs a model into segments and permits removal of extraneous print material and allows infiltrant penetration of the entire bone structure. Precise reassembly is facilitated by digitally generated fiducials. Infiltrant of choice was determined by expert assessment and subjected to objective mechanical property assessment with comparison to cadaveric sheep bone. RESULTS: The printed bone models are highly realistic. Void space representation was excellent with 88% concordance between cadaveric bone and the resultant rapid-prototyped temporal bone model. Ultimately, cyanoacrylate with hydroquinone was determined to be the most appropriate infiltrant for both cortical and trabecular simulation. The mechanical properties of all tested infiltrants were similar to real bone. CONCLUSION: This model serves as an excellent replica of a human temporal bone for training and preoperative surgical rehearsal and can be dissected in a true-to-life fashion.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".