Imaging-Based 3-Dimensional Printing for Improved Maxillofacial Presurgical Planning: A Single Center Case Series
Bibliographic record
Abstract
PURPOSE: 3-D printing is an increasingly widespread technology that allows physical models to be constructed based on cross-sectional medical imaging data. We sought to develop a pipeline for production of 3-dimensional (3-D) models for presurgical planning and assess the value of these models for surgeons and patients. METHODS: In this institutional review board-approved, single-center case series, participating surgeons identified cases for 3-D model printing, and after obtaining patient consent, a 3-D model was produced for each of the 7 participating patients based on preoperative cross-sectional imaging. Each model was given to the surgeon to use during the surgical consent discussion and preoperative planning. Patients and surgeons completed questionnaires evaluating the quality and usefulness of the models. RESULTS: The 3-D models improved surgeon confidence in their operative approach, influencing the choice of operative approach in the majority of cases. Patients and surgeons reported that the model improved patient comprehension of the surgery during the consent discussion, including risks and benefits of the surgery. Model production time was as little as 4 days, and the average per-model cost was $350. CONCLUSIONS: 3-D printed models are useful presurgical tools from both surgeon and patient perspectives. Development of local hospital-based 3-D printing capabilities enables model production with rapid turnaround and modest cost, representing a value-added service for radiologists to offer their surgical colleagues.
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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 | 0.000 | 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 | 0.000 |
| 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".