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A bstract The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of s NN = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover | η | < 1 . 1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of $\\sqrt{{\\textrm{s}}_{\\textrm{NN}}}$ = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover |η| < 1.1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of $\\sqrt{{\\textrm{s}}_{\\textrm{NN}}}$ = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover |η| < 1.1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of$$ \\sqrt{{\\textrm{s}}_{\\textrm{NN}}} $$s NN = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover | η | < 1 . 1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

The pseudorapidity distribution of charged hadrons produced in Au + Au collisions at a center-of-mass energy of √s̅_̅(̅N̅N̅)̅ = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover |η| < 1.1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

Abstract The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of s NN$$ \\sqrt{{\\textrm{s}}_{\\textrm{NN}}} $$= 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover |η| < 1.1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

Background The World Health Organization (WHO) International Classification of Diseases and Related Health Problems (ICD) is used globally by 194 WHO member nations. It is used for assigning clinical diagnoses, providing the framework for reporting public health data, and to inform the organization and reimbursement of health services. Guided by overarching principles of increasing clinical utility and global applicability, the 11th revision of the ICD proposes major changes that incorporate empirical advances since the previous revision in 1992. To test recommended changes in the Mental, Behavioral, and Neurodevelopmental Disorders chapter, multiple vignette-based case-controlled field studies have been conducted which examine clinicians’ ability to accurately and consistently use the new guidelines and assess their overall clinical utility. This manuscript reports on the results from the study of the proposed ICD-11 guidelines for feeding and eating disorders (FEDs). Method Participants were 2288 mental health professionals registered with WHO’s Global Clinical Practice Network. The study was conducted in Chinese, English, French, Japanese, and Spanish. Clinicians were randomly assigned to apply either the ICD-11 or ICD-10 diagnostic guidelines for FEDs to a pair of case vignettes designed to test specific clinical questions. Clinicians selected the diagnosis they thought was correct for each vignette, evaluated the presence of each essential feature of the selected diagnosis, and the clinical utility of the diagnostic guidelines. Results The proposed ICD-11 diagnostic guidelines significantly improved accuracy for all FEDs tested relative to ICD-10 and attained higher clinical utility ratings; similar results were obtained across all five languages. The inclusion of binge eating disorder and avoidant-restrictive food intake disorder reduced the use of residual diagnoses. Areas needing further refinement were identified. Conclusions The proposed ICD-11 diagnostic guidelines consistently outperformed ICD-10 in distinguishing cases of eating disorders and showed global applicability and appropriate clinical utility. These results suggest that the proposed ICD-11 guidelines for FEDs will help increase accuracy of public health data, improve clinical diagnosis, and enhance health service organization and provision. This is the first time in the revision of the ICD that data from large-scale, empirical research examining proposed guidelines is completed in time to inform the final diagnostic guidelines.

The Brief Symptom Inventory (BSI), developed by Derogatis in 1975, represents an important standardized screening instrument that enables one to quantitatively assess psychological distress and psychiatric disorders. The BSI is a 53-item self-report scale, measuring nine dimensions that can be summed up to reflect three global indices, including the General Severity Index (GSI). In the era of new information and communication technologies, nomophobia (\"no mobile phobia\") is an emerging disorder, characterized by the fear of being out of mobile phone contact. Nothing is known, however, about the factor structure and reliability of the BSI in a population of nomophobic subjects. This study aimed at addressing this gap in knowledge. A sample of 403 subjects aged 27.91±8.63 years (160 males, 39.7% of the entire sample, and 243 females, 60.3%), recruited via snowball sampling, volunteered to take part in the study. The Italian versions of the Nomophobia questionnaire and the BSI were administered. Exploratory factor analyses, confirmatory factor analyses, and clustering analysis were carried out together with correlation analysis, analysis of variance, and multivariate regression analysis. For each BSI subscale, scores were significantly higher than the norms. The nine subscales exhibited acceptable-to-good Cronbach's alpha coefficients, varying from 0.733 for psychoticism to 0.875 for depression. Overall, the reliability of the entire instrument proved to be excellent (alpha coefficient=0.972). Furthermore, all BSI subscales as well as BSI synthetic indexes correlated with nomophobia in a significant way. Stratifying the population according to the severity of nomophobia (mild, 206 individuals, 51.1% of the sample; moderate, 167 subjects, 41.4%; and severe, 30 individuals, 7.4%), the GSI score could distinguish ( <0.001) between mild and moderate (0.99±0.71 vs 1.32±0.81) and between mild and severe (0.99±0.71 vs 1.54±0.79) nomophobia, although not between moderate and severe nomophobia ( >0.05). Similar patterns could be found for the other subscales of the BSI. Finally, looking at the fit indexes, the second-order 9-factor model best fit the data compared with the Derogatis 1-factor model. The findings of our study show that the BSI is a reliable and valid instrument with acceptable psychometric properties, and can be administered to populations of nomophobic subjects.

Most research on health systems examines contemporary problems within one, or at most a few, countries. Breaking with this tradition, we present a series of case studies in a book written by key policymakers, scholars and experts, looking at health systems and their projected successes to 2030. Healthcare Systems: Future Predictions for Global Care includes chapters on 52 individual countries and five regions, covering a total of 152 countries. Synthesised, two key contributions are made in this compendium. First, five trends shaping the future healthcare landscape are analysed: sustainable health systems; the genomics revolution; emerging technologies; global demographics dynamics; and new models of care. Second, nine main themes arise from the chapters: integration of healthcare services; financing, economics and insurance; patient-based care and empowering the patient; universal healthcare; technology and information technology; aging populations; preventative care; accreditation, standards, and policy; and human development, education and training. These five trends and nine themes can be used as a blueprint for change. They can help strengthen the efforts of stakeholders interested in reform, ranging from international bodies such as the World Health Organization, the International Society for Quality in Health Care and the World Bank, through to national bodies such as health departments, quality and safety agencies, non-government organisations (NGO) and other groups with an interest in improving healthcare delivery systems. This compendium offers more than a glimpse into the future of healthcare−it provides a roadmap to help shape thinking about the next generation of caring systems, extrapolated over the next 15 years.

AbstractObjectiveTo evaluate the accuracy of the depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) to screen for major depression among people with physical health problems.DesignSystematic review and individual participant data meta-analysis.Data sourcesMedline, Medline In-Process and Other Non-Indexed Citations, PsycInfo, and Web of Science (from inception to 25 October 2018).Review methodsEligible datasets included HADS-D scores and major depression status based on a validated diagnostic interview. Primary study data and study level data extracted from primary reports were combined. For HADS-D cut-off thresholds of 5-15, a bivariate random effects meta-analysis was used to estimate pooled sensitivity and specificity, separately, in studies that used semi-structured diagnostic interviews (eg, Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders), fully structured interviews (eg, Composite International Diagnostic Interview), and the Mini International Neuropsychiatric Interview. One stage meta-regression was used to examine whether accuracy was associated with reference standard categories and the characteristics of participants. Sensitivity analyses were done to assess whether including published results from studies that did not provide raw data influenced the results.ResultsIndividual participant data were obtained from 101 of 168 eligible studies (60%; 25 574 participants (72% of eligible participants), 2549 with major depression). Combined sensitivity and specificity was maximised at a cut-off value of seven or higher for semi-structured interviews, fully structured interviews, and the Mini International Neuropsychiatric Interview. Among studies with a semi-structured interview (57 studies, 10 664 participants, 1048 with major depression), sensitivity and specificity were 0.82 (95% confidence interval 0.76 to 0.87) and 0.78 (0.74 to 0.81) for a cut-off value of seven or higher, 0.74 (0.68 to 0.79) and 0.84 (0.81 to 0.87) for a cut-off value of eight or higher, and 0.44 (0.38 to 0.51) and 0.95 (0.93 to 0.96) for a cut-off value of 11 or higher. Accuracy was similar across reference standards and subgroups and when published results from studies that did not contribute data were included.ConclusionsWhen screening for major depression, a HADS-D cut-off value of seven or higher maximised combined sensitivity and specificity. A cut-off value of eight or higher generated similar combined sensitivity and specificity but was less sensitive and more specific. To identify medically ill patients with depression with the HADS-D, lower cut-off values could be used to avoid false negatives and higher cut-off values to reduce false positives and identify people with higher symptom levels.Trial registrationPROSPERO CRD42015016761.