EPSM 2022, Engineering and Physical Sciences in Medicine
Bibliographic record
Abstract
Medical physicists are often ''custodians'' of quality within their workplaces [1], having a scope of practice that covers many aspects of quality management, including assessment and mitigation of risk, the evaluation of new technologies, quality control and assurance testing, and ongoing monitoring and quality improvement [2].It is in this capacity that they often lead or contribute to the development of new medical devices or software to improve clinical practices.3D printing, open-source software development libraries, and the wealth of guidance available online have removed barriers of entry, accelerating these activities at point-of-care.Physicists, however, are not necessarily familiar with the regulatory environment surrounding the development and supply of these devices.In a 2022 ACPSEM webinar, 72% of attendees reported they were ''not at all'' or only ''slightly'' confident in their understanding of the definition of a medical device, and what is subject to TGA or Medsafe regulation [2].Anecdotally, similar uncertainties exist around what constitutes software as a medical device.This presentation will share the experiences of the Royal Brisbane and Women's Hospital and Herston Biofabrication Institute Cancer Care program in quality management of medical device development and achieving regulatory conformity.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.126 | 0.078 |
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".