Die Verordnung (EU) 2017/746 (IVDR) in der Praxis: Umsetzung von Anhang I in der Pathologie
Bibliographic record
Abstract
BACKGROUND: Regulation (EU) 2017/746 on in vitro diagnostic medical devices (IVDR) imposes several conditions on pathology institutes that develop and use in-house in vitro diagnostic medical devices (IH-IVDs). However, not all of these conditions need to be implemented immediately after the IVDR entered into force on 26 May 2022. Based on an amending regulation of the European Parliament and the Council of the European Union, the requirements for IH-IVDs will be phased in. Conformity with the essential safety and performance requirements of annex I must be ensured from May 2022. OBJECTIVES: With this article, we would like to present the practical implementation of the currently valid conditions for IH-IVDs at the Institute of Pathology at the University Hospital of Heidelberg, in order to provide possible assistance to other institutions. CONCLUSIONS: In addition to the intensive work on the requirements for IH-IVDs, several guidance documents and handouts provide orientation for the implementation and harmonisation of the requirements for healthcare institutions mentioned in Article 5 (5). Exchange in academic network structures is also of great importance for the interpretation and practical implementation of the IVDR. For university and nonuniversity institutions, ensuring conformity with the IVDR represents a further challenge in terms of personnel and time, in addition to the essential tasks of patient care, teaching and research and the further development of methods for optimal and targeted diagnostics, as well as the maintenance of the constantly evolving quality management system.
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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 | 0.075 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.027 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".