3D Printing for 21st Century Medical Learners: Opportunities for Innovative Research and Collaboration
Bibliographic record
Abstract
AbstractWith the commercialization of accessible 3D printers, using 3D printing for creation of personalized medical interventions has become a rapidly expanding area of research. In keeping with these developments, the Faculty of Medicine at the University of Ottawa has purchased 3D printers (Makerbot Replicator 2X and Ultimaker 2 Extended +) and launched a collaboration with Makerspace and the Health Sciences Library to investigate local opportunities to incorporate 3D printing into education, simulations and research. This article aims to summarize some of the recent developments in 3D printing and introduce readers to how one could use 3D printing for personalized medicine. RésuméAvec la venue de la commercialisation d’imprimantes 3D accessibles, l’emploi de l’impression 3D pour la création d’interventions médicales personnalisées est un domaine de recherche en développement rapide. Afin de rester à jour avec ces développements, la Faculté de Médecine de l’Université d’Ottawa s’est procuré des imprimantes 3D (Makerbot Replicator 2X et Ultimaker 2 Extended +) et a entamé une collaboration avec Makerspace et la Bibliothèque des Sciences de la Santé, pour examiner des opportunités locales visant à incorporer l’impression 3D à l’éducation, aux simulations et à la recherche. Cet article vise à résumer certains des développe- ments récents en impression 3D et à présenter aux lecteurs la manière dont celle-ci peut être utilisée pour la médecine personnalisée.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".