Regulating Certification Bodies in the Field of Medical Devices: The PIP Breast Implants Litigation and Beyond
Bibliographic record
Abstract
This article uses the breast implants scandal around the French producer Poly Implant Prothèse (PIP) to discuss the regulation of medical devices in EU law. Thereby, the specific focus is on the role of tort liability of certification bodies in complementing the public law regime of medical devices law. As tort law has not been harmonized yet at the level of EU law, national legal systems may produce different results; which indeed the PIP case demonstrates, with diverging judgments from French and German courts. Showing the deficiencies of the public law system of the Medical Devices Directive of 1993 as well as of the new Medical Devices Regulation of 2017, the article argues that tort liability is a necessary regulatory instrument to ensure that certification bodies live up to their duties under medical devices law and therefore a necessary instrument for the protection of the health and safety of patients.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.032 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".