TRANSFORMING P & O CARE WITH 3D PRINTING– MORE THAN MEETS THE EYE
Bibliographic record
Abstract
Many within the prosthetics and orthotics (P&O) industry are embracing 3D printing technology to produce better devices more efficiently, cost-effectively and to improve patient outcomes. 3D printing is here to stay, but how much will it transform P&O practices? This paper explores the state-of 3D printing technology as it applies to P&O and aims to highlight important considerations for bringing 3D printing into mainstream practice. The paper draws from recent published literature, as well as experiences stemming from ongoing efforts focused on implementing digital workflows and 3D printing into P&O care. The paper examines the topic from the technological, research, economics, funding, and clinical perspectives. While 3D printing and digital workflows have advantages over traditional methods (i.e. ability to design more complex parts, reprinting and reproduction of parts, less labour intensive) there are also challenges limiting adoption. First, despite recent advancements in 3D printing technology, gaps still exist in terms of the materials and processes. For example, cost-effectively fabricating devices that are concurrently strong and durable, allow for colourful designs, and are thermoformable are still being developed. Cost-wise, 3D printing may currently be more viable for small, or paediatric devices. There are also limited technical standards to ensure safe and durable devices are produced, as well as a lack of evidence and information about patient outcomes and operating costs. Nevertheless, a great amount of enthusiasm and momentum exists within the industry to innovate, and with it the potential for 3D printing to one day be central to mainstream P&O care. Given the many aspects of the P&O industry, collaboration and partnerships will facilitate learning from each other to advance and realize the potential of 3D printing sooner. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/42138/32197 How To Cite: Andrysek J, Ramdial S. Transforming P & O care with 3D printing– more than meets the eye. Canadian Prosthetics & Orthotics Journal. 2023; Volume 6, Issue 2, No.3. https://doi.org/10.33137/cpoj.v6i2.42138 Corresponding Author: Jan Andrysek, PhDBloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada.E-Mail: jan.andrysek@utoronto.caORCID ID: https://orcid.org/0000-0002-4976-1228
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 | 0.001 |
| 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".