Establishing a small company's medical device quality system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to chronicle a small company's path towards establishing a functioning, effective quality system for a medical device technology and to provide some “do‐it‐yourself” (DIY) tips learned along the way. Design/methodology/approach When a company comes up with an innovation in medical device technology, where can it go from there to transfer its product into the hands of consumers? If the technology is patented, the company has the option to license it. Alternatively, the company may want to move forward with further product development and marketing on its own (whether patented or not). Getting a medical device into any market typically requires regulatory approval, which cannot be obtained without a quality system. This paper focuses on the foundations of establishing a quality system and obtaining certification and regulatory approval in Canada, the EU, and the USA, and is directed towards small medical device manufacturers. It describes the process within four phases that cover the initial start up, implementation of procedures, certification and regulatory approval, and continual improvement. Findings Establishing a quality system is a monumental task for any company, but especially so for a small one. However, the benefits of implementing a quality system outweigh the initial setbacks associated with doing so. The descriptions of phases in tandem with the DIY tips presented in this paper are intended to be of help to a small medical device manufacturer wanting to bring their innovative technology to consumers within a major marketplace. Originality/value This is an original paper written for the Third Canadian Quality Congress.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it