Bibliographic record
Abstract
To provide safe and effective medical devices for patients in a timely manner, understanding the regulatory framework is a key element at an early stage of development. Medical devices in Japan are under the regulation of the Pharmaceuticals, Medical Devices, and Other Therapeutic Products Act (PMD Act). Japan has proactively incorporated Global Harmonization Task Force (GHTF) documents into its regulations to achieve regulatory convergence. The purpose of GHTF is to encourage convergence in regulatory requirements, practices and systems across the world. Currently, the International Medical Device Regulatory Forum (IMDRF) is continuing the work of GHTF and the current members are Australia, Brazil, Canada, China, the Russian Federation, the European Union, the United States, Singapore, South Korea and Japan. In this session, we will introduce the basic regulatory framework in Japan by comparing with GHTF documents especially for the premarketing review stage and how to use the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) standards in the regulation. Furthermore, revised PMD Act on November 2019 regarding medical devices will be introduced.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.433 | 0.329 |
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".