9 research outputs found

    Deckungsbeiträge

    Get PDF
    Der Deckungsbeitrag (DB) ist die Differenz zwischen Leistung und variablen Kosten eines Produktionszweiges. Der DB muss die Gemeinkosten abdecken. Gemeinkosten sind Kosten, die nicht schlüsselungsfrei einem bestimmten Kostenträger (Betriebszweig) zugeteilt werden können. Die Berechnung des Deckungsbeitrages erfolgt nicht nach genauen Regeln. Sie richtet sich vielmehr nach der konkreten Problemstellung. Je nach Planungshorizont werden beispielsweise mehr oder weniger Kostenpositionen miteinbezogen. Hingegen ist die Berechnung des vergleichbaren Deckungsbeitrages (alt DfE) exakt definiert

    An Frau Louise Ziegler, geborne Steiner im Steinberg : mit einem Andenken an ihre verstorbene Freundinn als Geschenk für die Wöchnerinn

    Full text link
    J. HanhartVerfasserangabe vom Ende des TextesSammlung Lauterbur

    Digital image analysis improves precision of PD-L1 scoring in cutaneous melanoma

    Full text link
    AIMS: Immune checkpoint inhibitors have become a successful treatment in metastatic melanoma. The high response rates in a subset of patients suggest that a sensitive companion diagnostic test is required. The predictive value of programmed death ligand 1 (PD-L1) staining in melanoma has been questioned due to inconsistent correlation with clinical outcome. Whether this is due to predictive irrelevance of PD-L1 expression or inaccurate assessment techniques remains unclear. The aim of this study was to develop a standardised digital protocol for the assessment of PD-L1 staining in melanoma and to compare the output data and reproducibility to conventional assessment by expert pathologists. METHODS AND RESULTS: In two cohorts with a total of 69 cutaneous melanomas, a highly significant correlation was found between pathologist-based consensus reading and automated PD-L1 analysis (r = 0.97, P < 0.0001). Digital scoring captured the full diagnostic spectrum of PD-L1 expression at single cell resolution. An average of 150 472 melanoma cells (median 38 668 cells; range = 733-1 078 965) were scored per lesion. Machine learning was used to control for heterogeneity introduced by PD-L1-positive inflammatory cells in the tumour microenvironment. The PD-L1 image analysis protocol showed excellent reproducibility (r = 1.0, P < 0.0001) when carried out on independent workstations and reduced variability in PD-L1 scoring of human observers. When melanomas were grouped by PD-L1 expression status, we found a clear correlation of PD-L1 positivity with CD8-positive T cell infiltration, but not with tumour stage, metastasis or driver mutation status. CONCLUSION: Digital evaluation of PD-L1 reduces scoring variability and may facilitate patient stratification in clinical practice

    Literatur

    Full text link

    VERZEICHNISSE

    Full text link
    corecore