Method, Material, and Machine: A Review for the Surgeon Using Three-Dimensional Printing for Accelerated Device Production
Bibliographic record
Abstract
BACKGROUND: Physicians are at the forefront of identifying innovative targets to address current medical needs. 3D printing technology has emerged as a state-of-the-art method of prototyping medical devices or producing patient-specific models that is more cost-efficient, with faster turnaround time, in comparison to traditional prototype manufacturing. However, initiating 3D printing projects can be daunting due to the engineering learning curve, including the number of methodologies, variables, and techniques for printing from which to choose. To help address these challenges, we sought to create a guide for physicians interested in venturing into 3D printing. STUDY DESIGN: All commercially available, plug-and-play, material and stereolithography printers costing less than $15,000 were identified via web search. Companies were contacted to obtain quotes and information sheets for all printer models. The qualifying printers' manufacturer specification sheets were reviewed, and pertinent variables were extracted. RESULTS: We reviewed 309 commercially available printers and materials and identified 118 printers appropriate for clinicians desiring plug-and-play models for accelerated device production. We synthesized this information into a decision-making tool to choose the appropriate parameters based on project goals. CONCLUSIONS: There is a growing clinical need for medical devices to reduce costs of care and increase access to personalized treatments; however, the learning curve may be daunting for surgeons. In this review paper, we introduce the "3Ms of 3D printing" for medical professionals and provide tools and data sheets for selection of commercially available, affordable, plug-and-play 3D printers appropriate for surgeons interested in innovation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".