Unlocking the value of 3D printed medical devices in hospitals and universities
Bibliographic record
Abstract
3D printing in Health Care Facilities (HCFs) has evolved from a set of experimental techniques and situational engineering applications employed at leading academic institutions to a relatively mature but expanding field with well-defined workflows and recognition at major medical societies. This project introduces the term ‘Final Anatomic Representation’ that refers to the final surface mesh files used in patient care. It also introduces the term ‘Patient Specific Realization’ to characterize how the Final Anatomic Representation is used, for example the creation of a 3D PDF, virtual reality display with shared experiences, augmented reality to include procedure simulation, or 3D printed parts. This project focuses on 3D printing in HCFs, and it includes a wide scope of use cases with literature support. Many intended uses have progressed to guideline support for appropriateness; these are organized by patient presentation or clinical scenario. One benefit of using clinical scenarios is that direct feedback can be translated from the engineering of 3D printed parts to the data generation from those parts used in the medical value equation. Continuing with the direct feedback, established value then supports guidelines for patient care such as clinical appropriateness, and those guidelines can then be applied to realize that value added for future patients who present with the same clinical scenarios.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".