Bibliographic record
Abstract
In this paper we briefly explored the history of 3D printing in prosthetics. We provided details of our own work developing 3D printing design tools from 2014-2020 noting how claims around prosthetist experience and knowledge have been supported and/or questioned in the development of new device production techniques. We ended by arguing for deeper attention to prosthetist knowledge and experience in the design of the growing 3D printing ecosystem, seeing this focus as necessary and important to preserve and support clinical prosthetist in the production of well-fitting and appropriate devices for patients. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/42175/33398 How To Cite: Ratto M, Southwick D. Prosthetist knowledge and 3D printing. Canadian Prosthetics & Orthotics Journal. 2024; Volume 7, Issue 2, No.5. https://doi.org/10.33137/cpoj.v7i2.42175 Corresponding Author: Matt Ratto, PhDFaculty of Information, University of Toronto, Toronto, Canada.E-Mail: matt.ratto@utoronto.caORCID ID: https://orcid.org/0000-0002-3554-4513
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".