L'harmonisation internationale des réglementations relatives aux dispositifs médicaux de diagnostic in vitro : intérêt des travaux du GHTF (Global Harmonization Task Force) et perspectives d'évolution de la réglementation
Bibliographic record
Abstract
In Vitro Diagnostic (IVD) medical device means a medical device, whether used alone or in combination, intended by the manufacturer for the in-vitro examination of specimens derived from the human body solely or principally to provide information for diagnostic, monitoring or compatibility purposes. International commercialization of IVD medical devices is taking place in a more and more demanding context especially regarding security and performance issues. However regulations related to this kind of product are specific to each country. It is in this context that was conceived in 1992 the Global Harmonization Task Force (GHTF), a voluntary group of representatives from five founding members (the European Union, the United States, Canada, Japan and Australia) whose goal was to promote the convergence of regulatory requirements of countries concerning medical devices and IVD medical devices. The objective of this thesis is to report on the status of current regulations applying to each of its founding members with a special focus on GHTF recommendations (definition, classification, conformity assessment procedure, STED, post market surveillance and labelling) in order to determine the influence of the GHTF on their national regulations and to estimate the level of convergence and interest for its work.
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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 | 0.110 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".