Bibliographic record
Abstract
Digitalisation is the megatrend in healthcare, not only since the pandemic. We are two European digital health experts and industry leaders in the field of orthotics and prosthetics (O&P) and in this article we explored what are the underlying trends driving the adoption of digitalisation for customisation of prosthetics & orthotics. We showed that several trends in 3D image capture (input step), 3D modelling (processing step) and 3D printing (output step) currently converge and thus fuel the rapid transformation of the O&P industry. In short outlooks, we rated the probability and timing of adoption rates across the upcoming couple of years. We furthermore reviewed the impact of boundary conditions set by regulators as well as the reimbursement system. Towards the end of this article, we outlined a digital scenario of the near future by following around an orthotist during her work. We finished with a call-to-action targeting regulators, payors, prosthetists/orthotists, and patients to enable such a desirable future. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36349/28332 How To Cite: Opitz M, Fröhlingsdorf P. The digital O&P workshop. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.15. https://doi.org/10.33137/cpoj.v4i2.36349 Corresponding Author: Manuel Opitz, MECURIS GmbH, Lindwurmstraße 11, 80337 München, Germany.E-Mail: mo@mecuris.comORCID number: https://orcid.org/0000-0001-6098-1881
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.162 | 0.050 |
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".