Bibliographic record
Abstract
Innovation is an important part of the prosthetic and orthotics (P&O) industry. Innovation has the potential to improve health care services and outcomes, however, it can also be a burden to the system if misdirected. This paper explores the interaction of innovation and economics within the P&O industry, focusing on its current state and future opportunities. Technological advancement, industry competition and pursuit of better patient outcomes drive innovation, while challenges in ensuring better P&O health care include lagging clinical evidence, limited access to data, and existing funding structures. There exists a greater need for inclusive models and frameworks for rehabilitation care, that focus on the use of appropriate technology as supported by research and evidence of effectiveness and cost-effectiveness. Additionally, innovative business models based on social entrepreneurism could open access to untapped and underserved markets and provide greater access to assistive technology. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/35203/28318 How To Cite: Andrysek J. The economics of innovation in the prosthetic and orthotics industry. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.7. https://doi.org/10.33137/cpoj.v4i2.35203 Corresponding Author: Jan Andrysek, PhDHolland Bloorview Kids Rehabilitation Hospital, Toronto, Canada.E-Mail: jan.andrysek@utoronto.ca; jandrysek@hollandbloorview.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 | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".