A Meta-Review about Medical 3D Printing
Bibliographic record
Abstract
Abstract In recent years, 3D printing (3DP) has gained importance in various fields. This technology has numerous applications, particularly in medicine. This contribution provides an overview on the state of the art of 3DP in medicine and showcases its current use in different medical disciplines and for medical education. In this meta-review, we provide a detailed listing of systematic reviews on this topic as this technology has become increasingly applied in modern medicine. We identified 134 relevant systematic reviews on medical 3DP in the medical search engine PubMed until 2023. 3DP has applications in various medical specialties, but is mainly used in orthopedics, oral and maxillofacial surgery, dentistry, cardiology and neurosurgery. In surgical contexts, the adoption of 3DP contributes to a reduction in operation time, reduced blood loss, minimized fluoroscopy time and an overall improved surgical outcome. Nevertheless, the primary use of 3DP is observed in non-invasive applications, particularly in the creation of patient-specific models (PSM). These PSMs enhance the visualization of patients' anatomy and pathology, thereby facilitating surgical planning and execution, medical education and patient counseling. The current significance of 3DP in medicine offers a compelling perspective on the potential for more individualized and personalized medical treatments in the future.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.020 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".