The Use of 3D Printing as an Educational Tool in Orthopaedics
Bibliographic record
Abstract
Background: Three-dimensional (3D) printing has proven to be effective in orthopaedic surgery, improving both surgical planning and outcomes. Despite its increasing use in surgical programs, reviews evaluating its educational impact are sparse. Therefore, the aim of this review was to provide educators with evidence-based findings on 3D printing's potential in training junior surgeons, as well as discuss its benefits in enhancing patient communication. Methods: A comprehensive search using PubMed and Web of Science databases was performed to identify articles related to orthopaedics, 3D printing, and education. After removing duplicates, 2,160 articles were screened, 152 underwent full-text review, and 50 met inclusion criteria. Articles discussed the impact of 3D-printed models on comprehension or surgical performance. Data on publication details, sample size, teaching focus, learning outcomes, costs, and conclusions were extracted. Learning effects in the control (didactic) and experimental (3DP) groups were compared. Results: In fracture management training, studies demonstrated significantly improved fracture classification accuracy, surgical performance, and interobserver classification agreement with 3D models compared with didactic learning and traditional imaging modalities. These benefits were particularly evident in cases of complex fractures and junior trainees. In arthroscopy, 3D-printed simulators improved procedural accuracy and were more cost-effective than virtual reality simulators and cadaveric laboratory results. Three-dimensionally printed simulators were also assessed for skills related to spine surgery, in which trainees demonstrated clear learning curve improvements for pedicle screw placement and osteotomy techniques, as well as a better understanding of vital paraspinal structures. The application of 3D printing in patient education was equally promising, as it facilitated the process of informed consent, ultimately promoting shared decision making. Conclusion: The use of 3D-printed models offers effective and customizable methods for developing essential surgical skills. Future research should focus on larger, more diverse study populations and should include long-term follow-up to better assess the impact of 3D printing on education and patient outcomes.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".