Utilizing Additive Manufacturing to Produce Organ Mimics and Imaging Phantoms
Bibliographic record
Abstract
The complex geometries and material properties necessary for generating accurate organ mimics require new procedures and methods to fully utilize current technologies. The increased accessibility of 3D printers, along with more specialized bioprinters, allow the creation of highly tunable models of various body parts. Three-dimensional printing can reduce lead-time on custom parts, produce structures based on imaging data in patients, and generate a test bench for novel surgical methods. This technical note will cover three unique case studes and offer insights for how 3D printing can be used for lab research. Each case follows a unique design process in comparison to traditional manufacturing workflows as they required significantly more iterative design. The strengths of different printing technologies, design choices, and structural/chemical requirements all influence the design process. Utilization of in-house manufacturing allows for greater flexibility and lower lead-times for novel research applications. Detailed discussions of these design processes will help reduce some of the major barriers to entry for these technologies and provide options for researchers working in the field.
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 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".