Priorities for medical device regulatory approval: a report from the European Society of Cardiology Cardiovascular Round Table
Bibliographic record
Abstract
The European Union (EU) Medical Device Regulation increased regulatory scrutiny to improve the safety and performance of new medical devices. An equally important goal is providing timely access to innovative devices to benefit patient care. The European Society of Cardiology strongly advocates for the evolution of the Medical Device Regulation system to facilitate priority access for innovative devices for unmet needs and orphan cardiovascular (CV) medical devices in EU countries. Although device approval is currently executed by Notified Bodies in the EU, it will be advantageous in the mid-term to consider a single EU regulatory agency for devices. In the short term, steps can be taken to transform the current system into a more efficient, predictable, cost-effective, and user-friendly service. Key strategies include the following: enhancing predictability of the approval process through use of early scientific advice from regulators; establishing unique regulatory pathways for CV orphan, paediatric, and innovative devices; promoting more efficient (re)certification of essential legacy CV devices; improving transparency of sponsor interactions with Notified Bodies; expanding the roles of the Expert Panels to assist in the approval of CV devices; promoting global regulatory harmonization, considering streamlined authorization of CV medical technologies across selected jurisdictions; developing an efficient system to monitor device safety; and ensuring funding for data collection platforms. Some strategies that could help include considering a pilot programme for joint approval processes of selected devices in partnership with other regions (i.e. US Food and Drug Administration); developing priority pathways for accelerated access to innovative or orphan devices; and increasing recognition of the importance of early feasibility studies in the EU.
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 | 0.064 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".