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Background: The aim of this project was to explore how physicians and dentists in Germany evaluate biostatistics/biometry in general as well as their training in it. A further aim was to determine the importance of the subject for the professional practice of physicians and dentists and to gain insights for teaching – during studies at university and beyond. Method: A total of 2,000 physicians and 700 dentists from Schleswig-Holstein in Germany were contacted by mail and asked to participate in an online survey. They were asked to provide information on their perception of the subject of biostatistics/biometry in general, in relation to work and to teaching during their own studies as well as information on sociodemographic data. The evaluation was explorative with representation in cross tables. In addition, the association of predictive variables with the attitude was estimated descriptively in a logistic regression. In order to examine differences between physicians and dentists with regard to the usefulness of biostatistics/biometry for practical work as well as with regard to their own training during their studies, cross tables were constructed and descriptive p-values were determined with the χ2 goodness-of-fit test or Fisher's exact tests. Results: The response rate was 13.67% (356/2,605). The subject was rated as difficult by 58.76% (208/354). 93.48% (330/353) stated that the subject is a necessary skill for a clinician involved in research, and 93.79% (332/354) rated the subject as important for evidence-based medicine. 20.00% (69/345) rated the teaching in biostatistics/biometry during their own studies as still useful today. 65.22% (225/345) expressed that they would like to understand more about the subject. Conclusion: The subject of biostatistics/biometry was rated as important for research, evidence-based medicine and a range of other medical activities, while many physicians and dentists expressed dissatisfaction with their teaching during their studies. Thus, further postgraduate training in biostatistics/biometry with appropriate practical relevance should be developed. [ABSTRACT FROM AUTHOR]

Introduction: According to the Master Plan 2020, science education will play a critical role in future medical curricula. Science modules have already been implemented at many locations. Other medical faculties will follow in the next few years, as legislation is expected to make recommendations of the national competence-based learning objectives curriculum for medicine (NKLM) mandatory. This article aims to present an implementation example from epidemiology and biometry as a contribution to the didactic discussions within the data sciences in medicine. Project description: We report on our experiences with a data analysis project for second-year medical students, which has been compulsory at the Faculty of Medicine and Health Sciences since 2019. The project is intended to train the scientific skills required from the subjects of epidemiology and biometry for student research projects. Emphasis is placed on responsible data handling, transparency, and reproducibility. For example, the writing of a statistical analysis plan is required prior to data access. Improved standardization of materials, optional use of the English language, and digital support will be implemented to help manage the project when student numbers increase. Discussion: The experience from five years is very positive, although a formal evaluation of the learning success is still pending. Current challenges concern staffing, additional time and supervision requirements for those students who do statistical programming with R, and improved integration into the medical curriculum. [ABSTRACT FROM AUTHOR]

In the 18th and 19th century crossing experiments using maize, ornamentals and peas were carried out in Europe and in the USA. Mendel knew at least a part of the literature when he published his famous paper "Experiments on Plant Hybridisation" with the original German title "Versuche über Pflanzenhybriden" in 1866. In contrast to his predecessors, Mendel recognised the constant segregation ratios appearing in the progenies of the crossings. After being unnoticed for decades, his fundamental theory of heredity was rediscovered and experimentally verified by de Vries, Correns and Tschermak in 1900. Shortly after the English translation of Mendel's work became available in 1901, Raphael Weldon, one of the founders of biometry, published his doubts on Mendel's data since all observed segregation ratios were extremely close to the expected ones. Ronald A. Fisher concluded in a paper from 1936 that a large part of Mendel's results must have been falsified to agree closely with the postulated segregation ratios. The Mendel-Fisher controversy was born. In 1966, 100 years after Mendel's original paper was released, Stern & Sherwood published a book comprising translations of Mendel's laws of inheritance, the rediscovery by de Vries and Correns and Fisher's paper. Subsequently, this led to the publication of numerous articles from different scientific disciplines on the Mendel-Fisher controversy. It may never be revealed, if it was simple luck, if Mendel sorted out plants, which did not represent the theory, or if he had an assistant "who knew too well what was expected", as Fisher speculated in 1936. Nonetheless, Mendel's work doubtlessly culminated in one of the most important breakthrough discoveries of the 19th century and is the starting point of modern genetics and plant breeding. [ABSTRACT FROM AUTHOR]

Die Bestimmung von pädiatrischen Referenzintervallen ist ein hochaktuelles Arbeitsgebiet der Laboratoriumsmedizin. Zu den Herausforderungen zählen vor allem kleine Fallzahlen und große Alterssprünge. Hier bietet die Biostatistik mit kontinuierlichen Referenzgrenzen und zlog-Werten interessante Lösungsansätze. Wir stellen kostenfreie Werkzeuge vor, die dabei helfen sollen, Erfahrungen mit den neuen Verfahren zu sammeln. [ABSTRACT FROM AUTHOR]

Medical data sciences receive attention as digitalization and artificial intelligence (AI) pose new challenges and opportunities in health care. Specific methods and skills derived from the data sciences have been driving essential developments in almost if not all areas of health care and medical research over many years. For example, evidence-based medicine is now a pillar of medical practice, combined with a stronger focus on reproducible and valid research and an improved understanding of scientific methods. The increased role of the data sciences in medical research and practice is reflected in the revised national competency-based learning objectives catalog for medicine (NKLM 2.0). Digitalization, research skills, evidence medicine and health promotion and prevention are integral parts of the curriculum from start to end. They relate to all subjects and topics in an interprofessional manner. This increase in relevance of the data sciences clearly calls for improved competencies in the clinico-theoretical disciplines previously combined as interdisciplinary domain 1 (Q1). Epidemiology, medical biometry and medical informatics will now contribute expertise throughout the whole course of studying medicine. These disciplines deal with digitalization, medical research competence, evidence-based medicine, and prevention. In addition, disease-related learning and many aspects of therapy, diagnostics, communication, and management benefit from cooperation with dedicated instruction in the medical data sciences by didactically trained experts. This article aims to support faculties and subject representatives during the implementation period of the NKLM 2.0 and beyond regarding data science skills. Tables provide an overview of essential learning objectives in epidemiology, biometry, and medical informatics across the different phases of the curriculum. In addition, we give recommendations for cooperation with other subject representatives. By this we wish to contribute to improving the medical curriculum based on graduate profile-oriented interdisciplinary-integrative teaching. [ABSTRACT FROM AUTHOR]

Background: The revised National Competence-Based Learning Objectives Catalogue for Medicine (NKLM 2.0) is available since 2021. It will be part of the new licensing regulations for physicians (ApprOÄ), which are to come into force in 2025, and will also form the basis for the subject catalogue for state examinations (GK). Aimof the report: Information on the status, structure, relevant learning objectives, their location in the NKLM and specific challenges are presented to support lecturers of epidemiology, biometrics andmedical informatics ("Q1 subjects") when revising teaching concepts to comply with the new regulations. Methods and content: This is a narrative report based on the published NKLM and experiences gathered during the revision phase. Following presentation of formal elements such as general structure and levels of competency, underlying concepts such as the Z-Curriculumand profile orientation are briefly explained. Subsequently, the chapter on science skills is presented in detail to show its relevance and challenges for epidemiology, medical biometry and medical informatics. Conclusion and outlook: In summary, the developments as presented in the NKLM 2.0, GK and ApprOÄ show the increasing importance of the data sciences in medical education. All subjects are challenged by the complexity and interdisciplinary character of the catalogue. [ABSTRACT FROM AUTHOR]

The article presents the discussion on biostatistics, an esteemed discipline at the crossroads of natural and social sciences. Topics include laboratory medicine, an inherently data-driven field showing new opportunities and challenges, necessitating the acquisition of novel statistical and digital competencies; and utilization of advanced statistical methods and computational tools holding the potential to enhance the accuracy, reliability.

Health Technology Assessment is one of the standard instruments in support of the decision-making to define the public health services both internationally and in the German health care system. Besides system- atic reviews, benefit-harm-analyses, health economic evaluations, and, decision-analytic modelling, especially epidemiological and biometrical questions and methods play a key role. From this perspective discus- sions on increased European cooperation including calls for wider harmonization are attracting greater interests. The overall aim is, to present at the GMDS workshop relevant information on this emerging field of harmonization in Europe across similarities and differences in the HTA process. Current developments around the composition of the EU-HTA Network are provided. This network accepts the challenge to define and establish a "Joint Work" across Europe. Special emphasis was placed on the discussion on "Harmonization of HTA: is it a threat or does it mean support?". Furthermore, methodolo- gical discussions and questions are being addressed: "Are Real World Data and Surrogates possible parameters for decision-making or HTA?" [ABSTRACT FROM AUTHOR]

During every summer term at Ulm University the obligatory course of biometry (Q1) is taught in a PC pool using the statistical software SAS Analyst. Short examinations are performed at each of the 6 course dates summing the results over the term. This examination principle has proved to be worthwhile, because a continuous learning of the students during the whole term can be achieved by this. Some problems have to be resolved before realization. Among others, the time problem within the exercise has to be solved, as well as the formal criteria which have to be met. In addition, more time will be needed for the correction, because all students get individual data for the exams and so a standardisation is not possible. To solve these problems we have implemented the following points into our examination tool: For every student a suitable dataset with auxiliary conditions concerning the evaluation is generated at random, a computer based input module is provided for the students' answers, we completed a programme for comparing the individual solutions with the students' inputs, additional manual scoring for graphics and texts is possible, and the results of exams can be brought together and presented in appropriate lists in an anonymous way. The new system has been developed with different software-tools, whereas the main aspects have been realized by the statistical software SAS as well as MS Office programmes MS Excel and MS Access. [ABSTRACT FROM AUTHOR]

The article reports on six workshops on the topics of reference interval checking, laboratory data standardization and color coding as well as biostatistics with Excel/VBA and R, organized by the Trillium Academy in 2019 in Germany.