Analysis on the registration and review system of emergency medical devices in China and abroad in the context of COVID-19
Bibliographic record
Abstract
Objective In the context of COVID-19, the domestic and foreign demand for emergency medical devices, such as medical masks and protective suits, is surging, and it is urgent to complete the registration and review of emergency medical devices with high efficiency and quality, which requires a mature and perfect registration and review system as the support. This paper aims to compare and analyze the domestic and foreign registration and review system of emergency medical device, summarize the good experience, and provide feasible suggestions for improving China's emergency medical device registration and review system. Method USA, Canada, Japan and the European Union were selected to make a comparative analysis with China from the aspects of legal system and emergency registration and review procedure by literature research, comparative analysis and other theoretical methods. Results The legal system and review mechanism of emergency medical device registration in China have been relatively perfect, but the safety and risk balance mechanism and the comprehensiveness of emergency management measures need to be further improved. Conclusion On the basis of maintaining its own institutional advantages, China should learn from foreign experience to further optimize the registration and review system of emergency medical devices, so as to improve the ability of response and implementation of China in public health emergencies.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".