Bibliographic record
Abstract
3D printing has an over forty-year history but has only become popular in the last fifteen years with the expiration of restrictive patents which allowed open access and unfettered innovation by a broad range of technology developers. During the last decade interest in prostheses made by 3D printing has grown in popularity. The interest in devices has followed the Gartner Hype Cycle as 3D printing companies and associated organizations have used popular claims about 3D printed prostheses to increase their own company’s popularity. These claims created unrealistic expectations which outran the early-stage limitations of the technology, causing disillusion. Currently, the industry is moving beyond these limitations and the field seems to be advancing at a sustainable rate. This article provides an understanding of the history of popular misconceptions surrounding the technology. It provides a basis for separating the myth from reality in 3D printing technology so the reader can question the popular preconceived ideas and find the real value. With a greater understanding of the past, one can apply lessons to present technology use and guide the direction of future 3D printing. This paper will also discuss lessons applicable to both high and low-income countries along with providing recommendations for the future development. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/42141/32201 How To Cite: Erenstone J. 3D printed prostheses: the path from hype to reality. Canadian Prosthetics & Orthotics Journal. 2023; Volume 6, Issue 2, No.4. https://doi.org/10.33137/cpoj.v6i2.42141 Corresponding Author: Jeffrey Erenstone, CPOMountain O & P Services, 7 Old Military Road, Lake Placid, NY USA.E-Mail: erenstone@gmail.comORCID ID: https://orcid.org/0000-0003-1015-9616
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 | 0.001 | 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".