The EORTC QLU-C10D distinguished better between cancer patients and the general population than PROPr and EQ-5D-5L in a cross-sectional study.

Health state utility (HSU) instruments for calculating quality-adjusted life years, such as the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility - Core 10 Dimensions (QLU-C10D), derived from the EORTC QLQ-30 questionnaire, the Patient-Reported Outcome Measurement Information System (PROMIS) preference score (PROPr), and the EuroQoL-5-Dimensions-5-Levels (EQ-5D-5L), yield different HSU values due to different modeling and different underlying descriptive scales. For example the QLU-C10D includes cancer-relevant dimensions such as nausea. This study aimed to investigate how these differences in descriptive scales contribute to differences in HSU scores by comparing scores of cancer patients receiving chemotherapy to those of the general population. EORTC QLU-C10D, PROPr, and EQ-5D-5L scores were obtained for a convenience sample of 484 outpatients of the Department of Oncology, Charité - Universitätsmedizin Berlin, Germany. Convergent and known group's validity were assessed using Pearson's correlation and intraclass correlation coefficients (ICC). We assessed each descriptive dimension score's discriminatory power and compared them to those of the general population (n > 1000) using effect size (ES; Cohen's d) and area under the curve (AUC). The mean scores of QLU-C10D (0.64; 95% CI 0.62-0.67), PROPr (0.38; 95% CI 0.36-0.40), and EQ-5D-5L (0.72; 95% CI 0.70-0.75) differed significantly, irrespective of sociodemographic factors, condition, or treatment. Conceptually similar descriptive scores as obtained from the HSU instruments showed varying degrees of discrimination in terms of ES and AUC between patients and the general population. The QLU-C10D and its dimensions showed the largest ES and AUC. The QLU-C10D and its domains distinguished best between health states of the two populations, compared to the PROPr and EQ-5D-5L. As the EORTC Core Quality of Life Questionnaire (QLQ-C30) is widely used in clinical practice, its data are available for economic evaluation. The assessment of dimensions of health-related quality of life (HRQoL), such as physical functioning or depression, is important to cancer patients and physicians for treatment and side effect monitoring. Descriptive HRQoL is measured by patient-reported outcomes measures (PROM). The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire and the Patient-Reported Outcome Measurement Information System (PROMIS) are the most common PROM in the clinical HRQoL assessment. In recent years, multidimensional preference-based HRQoL measures were developed using these PROM as dimensions. These preference-based measures, also referred to as health state utility (HSU) scores, are needed for economic evaluations of treatments. The QLQ-C30's corresponding HSU score is the Quality of Life Utility measure-Core 10 Dimensions (QLU-C10D), and PROMIS' HSU score is the PROMIS preference score (PROPr). Both new HSU scores are frequently compared to the well-established EuroQoL-5-dimensions-5-levels (EQ-5D-5L). They all conceptualize HSU differently, as they assess different dimensions of HRQoL und use different models. Both the QLU-C10D and the PROPr have thus shown systematic differences to the EQ-5D-5L but these were largely consistent across the subgroups. Convergent and known groups validity can therefore be considered established. However, as HSU is a multidimensional construct, it remains unclear how differences in its dimensions, for example, its descriptive scales, contribute to differences in HSU scores. This is of importance as it is the descriptive scales that measure clinical HRQoL. We investigated this question by assessing each dimension's ability to distinguish between a sample of 484 cancer patients and the German general population. We could show that the ability to distinguish depended on the domain: for example, for depression, the QLU-C10D and EQ-5D-5L distinguished clearer, while for physical function, PROMIS did. Overall, the QLU-C10D and its dimensions distinguish best between cancer patients and general population.

Döhmen A ，Obbarius A ，Kock M ，Nolte S ，Sidey-Gibbons CJ ，Valderas JM ，Rohde J ，Rieger K ，Fischer F ，Keilholz U ，Rose M ，Klapproth CP ... -  《-》

The Cancer-Specific Health Economic Measure QLU-C10D is Valid and Responsive for Assessing Health Utility in Patients with Thyroid Cancer.

Pilz MJ ，Seyringer S ，Singer S ，Ioannidis G ，Sykiotis GP ，Arraras JI ，Husson O ，Iakovou I ，Fanetti G ，Führer D ，Inhestern J ，Kiyota N ，Locati LD ，Pinto M ，Gama RR ，King MT ，Norman R ，Gamper EM ，EORTC Quality of Life Group ... -  《-》

Erratum: Eyestalk Ablation to Increase Ovarian Maturation in Mud Crabs.

《Jove-Journal of Visualized Experiments》

Assessing the reliability of a novel cancer-specific multi-attribute utility instrument (FACT-8D) and comparing its validity to EQ-5D-5L in colorectal cancer patients.

To examine the test-retest reliability of the Functional Assessment of Cancer Therapy - 8 Dimension (FACT-8D) for the first time, and to conduct a head-to-head comparison of the distribution properties and validity between the FACT-8D and EQ-5D-5L in Colorectal Cancer (CRC) Patients. We conducted a longitudinal study on Chinese CRC patients, employing Functional Assessment of Cancer Therapy-General (FACT-G) and EQ-5D-5L at baseline, and FACT-G during follow-up (2-7 days from baseline). Utility scores for FACT-8D were derived from all available value sets (Australia, Canada and USA), while EQ-5D-5L scores were obtained from corresponding value sets for various countries. We assessed convergent validity using pairwise polychoric correlations between the FACT-8D and EQ-5D-5L; known-groups validity by discriminating participants' clinical characteristics, and effect size (ES) was tested; test-retest reliability for FACT-8D using kappa and weighted Kappa for choice consistency, and intraclass correlation coefficient (ICC) and Bland-Altman method for utility consistency. Among the 287 patients with CRC at baseline, 131 were included in the retest analysis. The utility scores of FACT-8D were highly positively correlated with EQ-5D-5L across various country value sets (r = 0.65-0.77), and most of the dimensions of FACT-8D and EQ-5D-5L were positively correlated. EQ-5D-5L failed to discriminate known-groups in cancer stage across all value sets, whereas both were significant in FACT-8D (ES = 0.35-0.48, ES = 0.38-0.52). FACT-8D showed good test-retest reliability (Cohen's weighted Kappa = 0.494-0.722, ICC = 0.748-0.786). The FACT-8D can be used as a valid and reliable instrument for clinical evaluation of patients with CRC, outperforming EQ-5D-5L in differentiating clinical subgroups and showing promise for cancer practice and research.

Cao Y ，Zhang H ，Luo N ，Li H ，Cheng LJ ，Huang W ... -  《-》

Exposure to hate in online and traditional media: A systematic review and meta-analysis of the impact of this exposure on individuals and communities.

People use social media platforms to chat, search, and share information, express their opinions, and connect with others. But these platforms also facilitate the posting of divisive, harmful, and hateful messages, targeting groups and individuals, based on their race, religion, gender, sexual orientation, or political views. Hate content is not only a problem on the Internet, but also on traditional media, especially in places where the Internet is not widely available or in rural areas. Despite growing awareness of the harms that exposure to hate can cause, especially to victims, there is no clear consensus in the literature on what specific impacts this exposure, as bystanders, produces on individuals, groups, and the population at large. Most of the existing research has focused on analyzing the content and the extent of the problem. More research in this area is needed to develop better intervention programs that are adapted to the current reality of hate. The objective of this review is to synthesize the empirical evidence on how media exposure to hate affects or is associated with various outcomes for individuals and groups. Searches covered the period up to December 2021 to assess the impact of exposure to hate. The searches were performed using search terms across 20 databases, 51 related websites, the Google search engine, as well as other systematic reviews and related papers. This review included any correlational, experimental, and quasi-experimental study that establishes an impact relationship and/or association between exposure to hate in online and traditional media and the resulting consequences on individuals or groups. Fifty-five studies analyzing 101 effect sizes, classified into 43 different outcomes, were identified after the screening process. Initially, effect sizes were calculated based on the type of design and the statistics used in the studies, and then transformed into standardized mean differences. Each outcome was classified following an exhaustive review of the operational constructs present in the studies. These outcomes were grouped into five major dimensions: attitudinal changes, intergroup dynamics, interpersonal behaviors, political beliefs, and psychological effects. When two or more outcomes from the studies addressed the same construct, they were synthesized together. A separate meta-analysis was conducted for each identified outcome from different samples. Additionally, experimental and quasi-experimental studies were synthesized separately from correlational studies. Twenty-four meta-analyses were performed using a random effects model, and meta-regressions and moderator analyses were conducted to explore factors influencing effect size estimates. The 55 studies included in this systematic review were published between 1996 and 2021, with most of them published since 2015. They include 25 correlational studies, and 22 randomized and 8 non-randomized experimental studies. Most of these studies provide data extracted from individuals (e.g., self-report); however, this review includes 6 studies that are based on quantitative analysis of comments or posts, or their relationship to specific geographic areas. Correlational studies encompass sample sizes ranging from 101 to 6829 participants, while experimental and quasi-experimental studies involve participant numbers between 69 and 1112. In most cases, the exposure to hate content occurred online or within social media contexts (37 studies), while only 8 studies reported such exposure in traditional media platforms. In the remaining studies, the exposure to hate content was delivered through political propaganda, primarily associated with extreme right-wing groups. No studies were removed from the systematic review due to quality assessment. In the experimental studies, participants demonstrated high adherence to the experimental conditions and thus contributed significantly to most of the results. The correlational and quasi-experimental studies used consistent, valid, and reliable instruments to measure exposure and outcomes derived from well-defined variables. As with the experimental studies, the results from the correlation and quasi-experimental studies were complete. Meta-analyses related to four dimensions were performed: Attitudinal changes, Intergroup dynamics, Interpersonal behaviors, and Psychological effects. We were unable to conduct a meta-analysis for the "Political Beliefs" dimension due to an insufficient number of studies. In terms of attitude changes, exposure to hate leads to negative attitudes (d Ex = 0.414; 95% confidence interval [CI] = 0.005, 0.824; p < 0.05; n = 8 and d corr = 0.322; 95% CI = 0.14, 0.504; p < 0.01; n = 2) and negative stereotypes (d Ex = 0.28; 95% CI = -0.018, 0.586; p < 0.10; n = 9) about individuals or groups with protected characteristics, while also hindering the promotion of positive attitudes toward them (d exp = -0.227; 95% CI = -0.466, 0.011; p < 0.10; n = 3). However, it does not increase support for hate content or political violence. Concerning intergroup dynamics, exposure to hate reduces intergroup trust (d exp = -0.308; 95% CI = -0.559, -0.058; p < 0.05; n = 2), especially between targeted groups and the general population, but has no significant impact on the perception of discrimination among minorities. In the context of Interpersonal behaviors, the meta-analyses confirm a strong association between exposure to hate and victimization (d corr = 0.721; 95% CI = 0.472, 0.97; p < 0.01; n = 3) and moderate effects on online hate speech perpetration (d corr = 0.36; 95% CI = -0.028, 0.754; p < 0.10; n = 2) and offline violent behavior (d corr = 0.47; 95%CI = 0.328, 0.612; p < 0.01; n = 2). Exposure to online hate also fuels more hate in online comments (d = 0.51; 95% CI = 0.034-0.984; p < 0.05; n = 2) but does not seem to affect hate crimes directly. However, there is no evidence that exposure to hate fosters resistance behaviors among individuals who are frequently subjected to it (e.g. the intention to counter-argue factually). In terms of psychological consequences, this review demonstrates that exposure to hate content negatively affects individuals' psychological well-being. Experimental studies indicate a large and significant effect size concerning the development of depressive symptoms due to exposure (d exp = 1.105; 95% CI = 0.797, 1.423; p < 0.01; n = 2). Additionally, a small effect size is observed concerning the link between exposure and reduced life satisfaction(d corr = -0.186; 95% CI = -0.279, -0.093; p < 0.01; n = 3), as well as increased social fear regarding the likelihood of a terrorist attack (d corr = -0.206; 95% CI = 0.147, 0.264; p < 0.01 n = 5). Conversely, exposure to hate speech does not seem to generate or be linked to the development of negative emotions related to its content. This systematic review confirms that exposure to hate in online and in traditional media has a significant negative impact on individuals and groups. It emphasizes the importance of taking these findings into account for policymaking, prevention, and intervention strategies. Hate speech spreads through biased commentary and perceptions, normalizing prejudice and causing harm. This not only leads to violence, victimization, and perpetration of hate speech but also contributes to a broader climate of hostility. Conversely, this research suggests that people exposed to this type of content do not show increased shock or revulsion toward it. This may explain why it is easily disseminated and often perceived as harmless, leading some to oppose its regulation. Focusing efforts solely on content control may then have a limited impact in driving substantial change. More research is needed to explore these variables, as well as the relationship between hate speech and political beliefs and the connection to violent extremism. Indeed, we know very little about how exposure to hate influences political and extremist views.

Madriaza P ，Hassan G ，Brouillette-Alarie S ，Mounchingam AN ，Durocher-Corfa L ，Borokhovski E ，Pickup D ，Paillé S ... -  《-》