Intersektionalität in der Internetforschung

Carstensen, Tanja; Winker, Gabriele

doi:10.5771/1615-634x-2012--3

Cited by 6 publications

(6 citation statements)

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“…The Spearman correlation test on the percentage of PMN was calculated within each evaluation method to assess the intra-method repeatability (function cor of the package Hmisc) [25]. Lin's concordance correlation coefficient (CCC, ρc) was calculated using the function epi.ccc of the package epi.R [26] to assess the inter-method agreement on the percentage of PMN among evaluation methods. The function confusionMatrix of the package caret [27] was used to determine Cohen's kappa agreement, sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and accuracy to assess the agreement within and among evaluation methods at three different PMN cut-off points (�1, >5, and >10% PMN).…”

Section: Plos Onementioning

confidence: 99%

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Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows

Sadeghi

Braun²,

Panti³

et al. 2022

PLoS ONE

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The assessment of polymorphonuclear leukocyte (PMN) proportions (%) of endometrial samples is the hallmark for subclinical endometritis (SCE) diagnosis. Yet, a non-biased, automated diagnostic method for assessing PMN% in endometrial cytology slides has not been validated so far. We aimed to validate a computer vision software based on deep machine learning to quantify the PMN% in endometrial cytology slides. Uterine cytobrush samples were collected from 116 postpartum Holstein cows. After sampling, each cytobrush was rolled onto three different slides. One slide was stained using Diff-Quick, while a second was stained using Naphthol (golden standard to stain PMN). One single observer evaluated the slides twice at different days under light microscopy. The last slide was stained with a fluorescent dye, and the PMN% were assessed twice by using a fluorescence microscope connected to a smartphone. Fluorescent images were analyzed via the Oculyze Monitoring Uterine Health (MUH) system, which uses a deep learning-based algorithm to identify PMN. Substantial intra-method repeatabilities (via Spearman correlation) were found for Diff-Quick, Naphthol, and Oculyze MUH (r = 0.67 to 0.76). The intra-method agreements (via Kappa value) at ≥1% PMN (κ = 0.44 to 0.47) were lower than at >5 (κ = 0.69 to 0.78) or >10% (κ = 0.67 to 0.85) PMN cut-offs. The inter-method repeatabilities (via Lin’s correlation) were also substantial, and values between Diff-Quick and Oculyze MUH, Naphthol and Diff-Quick, and Naphthol and Oculyze MUH were 0.68, 0.69, and 0.77, respectively. The agreements among evaluation methods at ≥1% PMN were weak (κ = 0.06 to 0.28), while it increased at >5 (κ = 0.48 to 0.81) or >10% (κ = 0.50 to 0.65) PMN cut-offs. To conclude, deep learning-based algorithms in endometrial cytology are reliable and useful for simplifying and reducing the diagnosis bias of SCE in dairy cows.

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Section: Plos Onementioning

confidence: 99%

“…Quick. Correlation and Bland-Altman plots were created to visualize the comparison within and among evaluation methods (packages ggplot2 and epi.R) [26,30].…”

Section: Plos Onementioning

confidence: 99%

Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows

Sadeghi

Braun²,

Panti³

et al. 2022

PLoS ONE

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“…Laut ARD/ZDF-Onlinestudie nutzen in der Bundesrepublik 88 Prozent der Frauen und 91 Prozent der Männer das Internet (Beisch et al 2019). Die etwas geringeren Nutzungszahlen bei Frauen sind inzwischen eher intersektional, also durch das Zusammenwirken von Geschlecht mit anderen Sozialkategorien wie Alter, Bildung und Einkommen zu erklären (Carstensen & Winker 2012). Ebenso nutzen inzwischen 83 Prozent der Männer und Frauen den digitalen Alleskönner Smartphone (Beisch et al 2019), wobei jüngere Altersgruppen fast flächendeckend damit versorgt sind: 92 Prozent der männlichen und 95 Prozent der weiblichen 12-bis 19-Jährigen besitzen ein eigenes Smartphone (JIM 2019).…”

Section: Geschlecht Und Digitale Technologienunclassified

„Ganz schön sozial-medial erschöpft…“. Eine qualitative Inhaltsanalyse der Berichterstattung über digitalen Stress und immanente Genderbezüge

Kinnebrock¹,

Nitsch²

2020

M&K

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Digitale Technologien und Medien haben zu weitreichenden Veränderungen im Arbeits- und Privatleben geführt. Dabei kann die Nutzung digitaler Medien aufgrund ständiger Erreichbarkeit, der Entgrenzung von Arbeits- und Privatleben oder auch Cybermobbing Stress verursachen und negative Folgen für die Gesundheit haben. Dieser Beitrag analysiert, welche Berichterstattungsmuster sich im medialen Diskurs über digitalen Stress zeigen und fokussiert im Besonderen auf das Gendering bei der Behandlung von digitalem Stress. Die durchgeführte qualitative Inhaltsanalyse deutschsprachiger Print- und Online-Beiträge (n=64) zeigt, dass der mediale Diskurs stark auf das Smartphone konzentriert ist. Digitaler Stress wird zwar in verschiedenartigsten Ausprägungen und Settings (am Arbeitsplatz wie im Privatleben) thematisiert, aber nur von einigen wenigen gegenderten Narrativen dominiert.

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“…Logistic regression analysis was calculated using R package stats ( R Core Team, 2016 ). ROC and area under the curve (AUC) were estimated using the R packages pROC ( Robin et al, 2011 ) and Epi ( Carstensen et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

Qiu

Sun

et al. 2017

PeerJ

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BackgroundOral tongue squamous cell carcinoma (OTSCC) is the most common subtype of oral cancer. A predictive gene signature is necessary for prognosis of OTSCC.MethodsFive microarray data sets of OTSCC from the Gene Expression Omnibus (GEO) and one data set from The Cancer Genome Atlas (TCGA) were obtained. Differentially expressed genes (DEGs) of GEO data sets were identified by integrated analysis. The DEGs associated with prognosis were screened in the TCGA data set by univariate survival analysis to obtain a gene signature. A risk score was calculated as the summation of weighted expression levels with coefficients by Cox analysis. The signature was used to distinguish carcinoma, estimated by receiver operator characteristic curves and the area under the curve (AUC). All were validated in the GEO and TCGA data sets.ResultsIntegrated analysis of GEO data sets revealed 300 DEGs. A 16-gene signature and a risk score were developed after survival analysis. The risk score was effective to stratify patients into high-risk and low-risk groups in the TCGA data set (P < 0.001). The 16-gene signature was valid to distinguish the carcinoma from normal samples (AUC 0.872, P < 0.001).DiscussionWe identified a useful 16-gene signature for prognosis of OTSCC patients, which could be applied to clinical practice. Further studies were needed to prove the findings.

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Intersektionalität in der Internetforschung

Cited by 6 publications

References 0 publications

Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows

Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows

„Ganz schön sozial-medial erschöpft…“. Eine qualitative Inhaltsanalyse der Berichterstattung über digitalen Stress und immanente Genderbezüge

A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

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