A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department

Accurate sepsis diagnosis is paramount for treatment decisions, especially at the emergency department (ED). To improve diagnosis, clinical decision support (CDS) tools are being developed with machine learning (ML) algorithms, using a wide range of variable groups. ML models can find patterns in Electronic Health Record (EHR) data that are unseen by the human eye. A prerequisite for a good model is the use of high-quality labels. Sepsis gold-standard labels are hard to define due to a lack of reliable diagnostic tools for sepsis at the ED. Therefore, standard clinical tools, such as clinical prediction scores (e.g. modified early warning score and quick sequential organ failure assessment), and claims-based methods (e.g. ICD-10) are used to generate suboptimal labels. As a consequence, models trained with these “silver” labels result in ill-trained models. In this study, we trained ML models for sepsis diagnosis at the ED with labels of 375 ED visits assigned by an endpoint adjudication committee (EAC) that consisted of 18 independent experts. Our objective was to evaluate which routinely measured variables show diagnostic value for sepsis. We performed univariate testing and trained multiple ML models with 95 routinely measured variables of three variable groups; demographic and vital, laboratory and advanced haematological variables. Apart from known diagnostic variables, we identified added diagnostic value for less conventional variables such as eosinophil count and platelet distribution width. In this explorative study, we show that the use of an EAC together with ML can identify new targets for future sepsis diagnosis research

Lernen zu Unterrichten – Veränderungen in den Einstellungsmustern von Lehramtsstudierenden während des Praxissemesters im Zusammenhang mit mentorieller Lernbegleitung und Kompetenzeinschätzung

Lehramtsstudierende unterscheiden sich in ihren Einstellungen, wie das Lernen zu Unterrichten am besten gelingt und welche Lernaktivitäten und Formen der Emotionsregulation dazu nötig sind. In der Studie wurden die Einstellungsmuster von N = 512 Studierenden (2. Mastersemester) zu Beginn und gegen Ende des Langzeitpraktikums Praxissemester mittels dem Inventory Learning to Teach Process (ILTP) erhoben und mögliche Zusammenhänge mit der Lernbegleitung durch Mentor/innen sowie mit der individuellen Kompetenzeinschätzung geprüft. Entgegen der theoretisch angenommen Muster wurden drei Muster (Vermeidend, Praxisorientiert, Vielseitig) identifiziert. Die Studie zeigt, dass eine hochwertig eingeschätzte mentorielle Begleitung mit dem Beibehalt oder dem Wechsel in das Muster Vielseitig in Relation steht und dass Kompetenzeinschätzungen Zusammenhänge mit den Einschätzungsmustern aufweisen. So empfinden diejenigen Studierenden den höchsten Kompetenzzuwachs, die während des Praxissemesters in das Muster Vielseitig wechseln bzw. in diesem verbleiben. Den geringsten Kompetenzzuwachs verzeichnen die Studierenden, die nach dem Praxissemester dem Muster Vermeidend zugeordnet wurden. Implikationen für die weitere Forschung zur Bedeutsamkeit im Umgang mit heterogenen Lerngruppen werden diskutiert

Can Teacher Education Make a Difference?

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Five teacher profiles in student-centred curricula based on their conceptions of learning and teaching

Contains fulltext : 170678.pdf (publisher's version ) (Open Access)BACKGROUND: Teachers' conceptions of learning and teaching are partly unconscious. However, they are critical for the delivery of education and affect students' learning outcomes. Lasting changes in teaching behaviour can only be realized if conceptions of teachers have been changed accordingly. Previously we constructed a questionnaire named COLT to measure conceptions. In the present study, we investigated if different teacher profiles could be assessed which are based on the teachers' conceptions. These teacher profiles might have implications for individual teachers, for faculty development activities and for institutes. Our research questions were: (1) Can we identify teacher profiles based on the COLT? (2) If so, how are these teacher profiles associated with other teacher characteristics? METHODS: The COLT questionnaire was sent electronically to all teachers in the first three years of the undergraduate curriculum of Medicine in two medical schools in the Netherlands with student-centred education. The COLT (18 items, 5 point Likert scales) comprises three scales: 'teacher centredness', 'appreciation of active learning' and 'orientation to professional practice'. We also collected personal information about the participants and their occupational characteristics. Teacher profiles were studied using a K-means cluster analysis and calculating Chi squares. RESULTS: The response rate was 49.4% (N = 319/646). A five-cluster solution fitted the data best, resulting in five teacher profiles based on their conceptions as measured by the COLT. We named the teacher profiles: Transmitters (most traditional), Organizers, Intermediates, Facilitators and Conceptual Change Agents (most modern). The teacher profiles differed from each other in personal and occupational characteristics. CONCLUSIONS: Based on teachers' conceptions of learning and teaching, five teacher profiles were found in student-centred education. We offered suggestions how insight into these teacher profiles might be useful for individual teachers, for faculty development activities and for institutes and departments, especially if involved in a curriculum reform towards student-centred education