Regulatory perspectives on post-market evidence generation schemes for high-risk medical devices: a systematic review
Bibliographic record
Abstract
The new European Medical Device Regulation has raised the bar for the clinical evaluation of medical devices to gain marketing authorization by Notified Bodies (NBs) regarding certificates of conformity in Europe. Restrictions applied for High-risk medical devices (HRMD) may require further evidence generation. Some other jurisdictions apply similar schemes that may be useful to the European Union. This systematic review focused on extracting lessons from similar schemes worldwide to the European context. A systematic review of peer-reviewed and gray literature was performed based on ‘Device approval’ and ‘conditional approval’ keywords. Databases such as Medline, Embase, and WoS retrieved documents assessed with the AMSTAR-2 checklist. A descriptive and narrative analysis was conducted detailed in CRD42023431233 – PROSPERO. We obtained eight documents where conditional approvals for High-risk medical devices in the United States of America, China, and Canada were subject to generate further evidence. In Europe, NBs impose restrictions or limitations to certificates of conformity instead. Further development of policies, supporting access to HRMD subject to further evidence generation, would help Europe in further defining the appropriate situations for the application of determined regulatory routes, to enhance access to HRMD with promising evidence and further evidence development. PROSPERO (CRD42023431233).
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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 | 0.264 | 0.562 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".