Legislative regulation and ethical governance of medical research in different European Union countries
Bibliographic record
Abstract
OBJECTIVE: To obtain information about the similarities and differences in regulating different types of medical research in the European Union (EU). METHODS: Web searches were performed from September 2009 to January 2011. Notes on pre-determined topics were systematically taken down from the web pages. The analysis relied only on documents and reports available on the web, reflecting the situation at the end of 2010. RESULTS: In several countries, regulatory legislation applied only to clinical trials on drugs and medical devices, in other states various types of research were also regulated but by laws different from those concerning trials, and in many countries, some research areas were not controlled by legislation at all. In very few countries was all medical research handled similarly from a legal point of view. The number of research ethics committees (RECs) in a single country varied from one to 264. Their areas of responsibility, working principles and length of time to grant research permission varied as well as the rules for obtaining informed consent from vulnerable groups. In 10 EU countries, there was no appeal mechanism after a negative decision by an REC. The RECs were not accountable to any organisation in five EU countries. CONCLUSIONS: There is a need for a fundamental debate regarding whether and which kinds of changes are needed for the further harmonisation of medical research governance in the EU and how cross-country medical research could be facilitated in the future.
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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 | 0.206 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".