Multi‐institutional Comparison of Temporal Bone Models: A Collaboration of the AAO‐HNSF 3D‐Printed Temporal Bone Working Group
Bibliographic record
Abstract
OBJECTIVE: The American Academy of Otolaryngology-Head and Neck Surgery Foundation's (AAO-HNSF's) 3D-Printed Temporal Bone Working Group was formed with the goal of sharing information and experience relating to the development of 3D-printed temporal bone models. The group conducted a multi-institutional study to directly compare several recently developed models. STUDY DESIGN: Expert opinion survey. SETTING: Temporal bone laboratory. METHODS: The working group convened in 2018. The various methods in which 3D virtual models had been created and printed in physical form were then shared and recorded. This allowed for comparison of the advantages, disadvantages, and costs of each method. In addition, a drilling event was held during the October 2018 AAO-HNSF Annual Meeting. Each model was drilled and evaluated by attending-level working group members using an 15-question Likert scale questionnaire. The models were graded on anatomic accuracy as well as their suitability as a simulation of both cadaveric and operative temporal bone drilling. RESULTS: The models produced for this study demonstrate significant anatomic detail and a likeness to human cadaver specimens for drilling and dissection. Models printed in standard resin material with a stereolithography printer scored highest in the evaluation, though the margin of difference was negligible in several categories. CONCLUSION: Simulated 3D temporal bones created through a number of printing methods have potential benefit in surgical training, preoperative simulation for challenging otologic cases, and the standardized testing of temporal bone surgical skills.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".