Medical Device Classification Guide
Bibliographic record
Abstract
The manufacturer should first clearly define the intended use of the device [1]. The next step is to consider the applicable classification regulations in force in the country where the product is going to be registered before being marketed. Most countries have their own classification scheme, such as Japan, People’s Republic of China [5], and India. The classification schemes of Canada and Australia/New Zealand resemble the European one, whereas the one in the United States is different. Efforts are under way to effect a global harmonization of classification. For instance, Study Group 1 of the Global Harmonization Task Force (GHTF/SG1/N15:2006) [8] has made progress and has been taken as a reference in some Asian countries such as Singapore, but it can still be noticed that systems differ and it is the up to the manufacturer to check and adapt its medical device class to the appropriate local classification scheme. Although different in many jurisdictions, they carry remarkable resemblance in most cases, having 4 categories of risks identified and treating in vitro diagnostics in their own right [3]. A large percentage of the products would be in the same class across the globe, but care should be taken to examine individual product classifications carefully. GHTF consolidates the classes as A to D, with A being the lowest risk, and D the highest.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.248 | 0.261 |
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".