3D Printing for Medical Applications: Current State of the Art and Perspectives during the COVID-19 Crisis
Bibliographic record
Abstract
The coronavirus SARS-CoV-2 pandemic has affected over one hundred million people worldwide and has resulted in over two million deaths. In addition to the toll that coronavirus takes on the health of humans infected with the virus and the potential long term effects of infection, the repercussions of the pandemic on the economy as well as on the healthcare system have been enormous. The global supply of equipment necessary for dealing with the pandemic experienced extreme stress as healthcare systems around the world attempted to acquire personal protective equipment for their workers and medical devices for treating COVID-19. This review describes how 3D printing is currently being used in life saving surgeries such as heart and lung surgery and how 3D printing can address some of the worldwide shortage of personal protective equipment, by examining recent trends of the use of 3D printing and how these technologies can be applied during and after the pandemic. We review the use of 3D printed models for treating the long term effects of COVID-19. We then focus on methods for generating face shields and different types of respirators. We conclude with areas for future investigation and application of 3D printing technology.
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 | 0.003 |
| 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".