Regulation of laboratory-developed tests and in-house in vitro diagnostic medical devices in the United States and the European Union—a comparative overview
Bibliographic record
Abstract
Process chains in medical diagnostic laboratories lead to an accurate diagnosis and consequently to optimized personalized therapy recommendations. In addition to approved commercial in vitro diagnostic medical devices, devices and tests manufactured and used within a single diagnostic laboratory play a decisive role in these process chains ensuring state-of-the-art diagnostics and consecutive best patient care. It is vital that the implementation and use of such in-house tests, processes and medical devices developed in diagnostic laboratories by health care professionals is quality assured and meets regulatory and legal requirements. Due to the complexity and variety of these tests as well as their dual use in routine care and clinical trials, a thorough understanding of these regulations and their underlying definitions is critical. This review provides a comparative overview of the current state of the regulation of laboratory-developed tests in the United States and in-house developed in vitro diagnostic medical devices in the European Union. It dissects and compares the relevant regulatory ecosystems to identify conceptual similarities and differences. Our work helps laboratories navigate the regulatory landscape and provides a basis for further discussions among key stakeholders including health care providers, payers, legislators and regulatory agencies.
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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 | 0.040 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| 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; 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".