3D Printing in Medicine: an introductory message from the Editor-in-Chief
Bibliographic record
Abstract
Personalized medicine and precision medicine are easier to conceptualize than define, and implementation can be even more challenging. 3D printing has intersected medicine to enable both. Personalized medicine is now delivered by “clinical modelers”, impassioned investigators are caretakers who model disease with 3D printing to define pathology, plan intervention, and treat patients. Creating, manipulating, and printing Standard Tessellation Language (STL) files is challenging; generating a hand-held model from a CT scan is harder than it has to be. Several diagnostic post-processing steps applied to the CT volume (collectively termed “3D visualization”) must be repeated to generate an STL file that is then 3D printed. Multiple software packages are typically required before the STL file is electronically placed on a separate build-tray software platform. In 5 years or less, the inefficiency of medical modeling will be a historical footnote. Current 3D printing publications are disparate. My group’s summary of the literature (submitted for publication in October 2014) attempted a comprehensive survey of the field stratified by organ section [1]. I personally apologize if your article was not included. However, those papers we did find and include spanned over 50 different journals. 3D Printing in Medicine is designed to provide a common platform peer-review platform. This forum is long overdue. The journal also addressed another missing piece: STL files are invited for submission and can be downloaded for free consumption by our readership. Those engaged in 3D printing are talented, and their creativity should be rewarded with development opportunities. 3D Printing in Medicine invites not only clinical studies, but also “concept papers” that will motivate and connect physicians, industry, engineers, and scientists in general. These papers will benefit from peer review and serve as a platform for funding that will drive further innovations. The journal will also address the question, “What defines a model that is clinically useful?” There are no 3D
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.026 | 0.038 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".