EXPLORING THE FUTURE OF PROSTHETICS AND ORTHOTICS: HARNESSING THE POTENTIAL OF 3D PRINTING
Bibliographic record
Abstract
This paper explores the transformative impact of 3D printing on Orthotics and Prosthetics, focusing on enhancing patient outcomes and clinical efficiency. Over the past decade, the integration of additive manufacturing has revolutionized device fabrication, particularly in diagnostic socket production, leading to significant time reductions in patient care. This article addresses challenges such as material limitations and the need for equivalent strength to traditional sockets, exploring the use of PETG filaments and advanced printers. It emphasizes the role of digital scanning and model modification technology, highlighting affordable solutions like Structure Sensor Scanners and iPhone-based capture systems in shaping the digital workflow. The importance of a standardized digital workflow in clinical settings is discussed, showcasing reduced practitioner time and improved patient care. The paper concludes by outlining ongoing efforts to enhance patient care through automation and flexible prints. In summary, this paper provides a concise overview of the impactful advancements in Orthotics and Prosthetics through 3D printing, highlighting its potential for improved clinical efficiency and patient outcomes. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/42140/32199 How To Cite: Gutierrez A.R. Exploring the future of prosthetics and orthotics: harnessing the potential of 3D printing. Canadian Prosthetics & Orthotics Journal. 2023; Volume 6, Issue 2, No.7. https://doi.org/10.33137/cpoj.v6i2.42140 Corresponding Author: Anthony R Gutierrez, CP/LPBionic Prosthetics and Orthotics Group LLC, Merrillville, Indiana, USA.E-Mail: tony@bionicpo.comORCID ID: https://orcid.org/0009-0002-2215-1750
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 | 0.001 | 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 | 0.001 |
| 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".