title: [Diagnosis in context - broadening the perspective].
abstract: In a primary care setting the diagnostic process typically starts with a symptom or sign reported by the patient. Primary care physicians face the challenge to consider a broad spectrum of possible aetiologies or differential diagnoses when choosing appropriate diagnostic tests. The classical diagnostic cross-sectional study investigates the accuracy of a diagnostic test or a combination of several tests in regard to just one target disease. The complexity facing the clinician remains unconsidered or is being split and presented in several parts which the clinician has to combine. In this paper we suggest a design for diagnostic studies that considers the requirements of diagnosis in primary care more comprehensively: the comprehensive diagnostic study. The essential characteristic of the design is the simultaneous consideration of the whole spectrum of relevant aetiologies when evaluating several diagnostic tests. We present single characteristics and specific features of this design in regard to research question, study sampling, index test, reference standard and analysis, and illustrate them using the example of a study investigating chest pain in primary care.
authors: Abteilung für Allgemeinmedizin, Präventive und Rehabilitative Medizin, Philipps Universität, Marburg. Electronic address: haasenri@staff.uni-marburg.de.
authors: Jörg
authors: J
authors: Haasenritter
authors: Annika
authors: A
authors: Viniol
authors: Annette
authors: A
authors: Becker
authors: Stefan
authors: S
authors: Bösner
authors: Eyke
authors: E
authors: Hüllermeier
authors: Robin
authors: R
authors: Senge
authors: Norbert
authors: N
authors: Donner-Banzhoff
mesh_terms: D001185:Artificial Intelligence; D002637:Chest Pain / Q000209:etiology*; D003430:Cross-Sectional Studies; D003937:Diagnosis, Differential; D003955:Diagnostic Tests, Routine; D019317:Evidence-Based Medicine; D005858:Germany; D006801:Humans; D007257:Information Theory; D008487:Medical History Taking; D010808:Physical Examination; D011237:Predictive Value of Tests; D011320:Primary Health Care; D012107:Research Design
keywords: Anamnese und Befund; Diagnose; Diagnosis; Informationstheorie; Maschinelles Lernen; Primärversorgung; Studiendesign; Symptomevaluation; information theory; machine learning; medical history taking; primary healthcare; research design; symptom assessment