Bibliographic record
Abstract
From 2014 until 2020, I participated in the development of a novel CAD/CAM system for lower-limb prosthetic sockets for use in Lower and Middle Income Countries (LMIC) orthopaedic clinical settings. This article provides an overview of the value principles that guided that work and the ways in which we attempted to support the clinical needs of our prosthetists and others in the clinical contexts. It will highlight how the health economic framework that is key to this special issue well describes the design choices we made in order to attend to the multiple levels of concerns and stakeholders we identified as key to success. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36188/28345 How To Cite: Ratto M. Designing a digital toolchain for prosthetics: A retrospective. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.16. https://doi.org/10.33137/cpoj.v4i2.36188 Corresponding Author: Matt Ratto, PhDFaculty of Information, University of 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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".