MétaCan
Menu
Back to cohort
Record W4387326541 · doi:10.1007/s00292-023-01231-3

Die Verordnung (EU) 2017/746 (IVDR) in der Praxis: Umsetzung von Anhang I in der Pathologie

2023· review· de· W4387326541 on OpenAlexaff
Andy Kahles, Hannah Goldschmid, Anna‐Lena Volckmar, Carolin Ploeger, Daniel Kazdal, Roland Penzel, Jan Budczies, Christa Flechtenmacher, Ulrich M. Gassner, Monika Brüggemann, Michael Vogeser, Peter Schirmacher, Albrecht Stenzinger

Bibliographic record

VenueDie Pathologie · 2023
Typereview
Languagede
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsSeagen (Canada)
FundersUniversitätsklinikum Heidelberg
KeywordsConformityParliamentEuropean unionHealth careConformity assessmentQuality (philosophy)Engineering managementComputer scienceOperations managementOperations researchBusinessPolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.054
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.054
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0000.008

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.

Opus teacher head0.319
GPT teacher head0.487
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDie PathologieSame topicClinical Laboratory Practices and Quality ControlFrench-language works237,207