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Background: A screening tool was devised to aid the diagnosis and treatment of ground‐glass nodules (GGNs). Methods: The current ambispective cohort study included retrospective collation of 20 variables synthesizing a patient's clinical characteristics, serum tumor markers, and CT results, which allowed division into noninvasive (benign, atypical adenomatous hyperplasia, and adenocarcinoma in situ) and invasive (minimally invasive and invasive adenocarcinomas) tumors to build a prediction nomogram and GGN screening scale. The model was verified internally. A prospective cohort of patients was randomly divided by envelope method into those assessed by the GGN screening scale and those assessed via CT values. The diagnostic efficiencies were compared to allow external verification of the model. Result: A total of 223 patients with 225 GGNs were recruited into the retrospective cohort between January 2021 and December 2022. Multivariable analysis showed sex, diameter, air bronchogram, and vessel convergence sign to be independent factors for prediction of noninvasive and invasive GGNs. Internal verification showed the model had a sensitivity of 70.7% and specificity of 75.0% with the Youden index at 0.457 and area under the curve (AUC) of 0.793 (95% CI: 0.734–0.852). Calibration curves indicated good internal stability (p = 0.357). Between January 2023 and March 2023, 147 patients with 148 GGNs were recruited into the prospective cohort. External verification showed the model had a sensitivity of 92.4% and specificity of 40.0% with the Youden index at 0.324 and AUC of 0.678 (95% CI: 0.509–0.847). Calibration curves indicated good external stability (p = 0.088). The scale was shown to have a sensitivity of 75.00%, specificity of 37.50%, positive predictive value of 91.53%, negative predictive value of 14.29%, and accuracy of 71.25%. Conclusion: The GGN screening scale has high sensitivity and accuracy, making it suitable for diagnosis of GGNs. [ABSTRACT FROM AUTHOR]

Stroke is a leading cause of death and disability worldwide. Early and comprehensive risk identification is essential for identifying individuals at high risk for stroke. This study aimed to evaluate each question in the new Stroke Risk Screening Scales (SRSS) and assess the domains for content relevance and representativeness. Initially, six stroke experts were invited to evaluate the SRSS questions. The content validity index (CVI), including the item-CVI (I-CVI) and the average-CVI (Ave-CVI), was then calculated. In our study, the acceptable standards for I-CVI and Ave-CVI were ≥0.78 and ≥0.9, respectively. The results showed that all invited experts accepted the invitation and evaluated the SRSS questions. The previous version of the SRSS consisted of 33 questions. Of these, 30 questions reached an I-CVI of ≥0.78, indicating good content validity. Three questions had an I-CVI of 0.67 and were considered invalid; thus, they were deleted. The overall instrument achieved an Ave-CVI of 0.95. Comprehensive SRSS are essential for effective stroke prevention planning. By facilitating the early identification of individuals at high risk for stroke, these scales help reduce the incidence and impact of stroke. The high content validity found in this study supports the reliability of the SRSS as a screening tool. In the future, implementing such validated scales in clinical practice can improve early intervention strategies, ultimately enhancing health outcomes and optimizing the use of healthcare resources. [ABSTRACT FROM AUTHOR]

ABSTRACT Background A screening tool was devised to aid the diagnosis and treatment of ground‐glass nodules (GGNs). Methods The current ambispective cohort study included retrospective collation of 20 variables synthesizing a patient's clinical characteristics, serum tumor markers, and CT results, which allowed division into noninvasive (benign, atypical adenomatous hyperplasia, and adenocarcinoma in situ) and invasive (minimally invasive and invasive adenocarcinomas) tumors to build a prediction nomogram and GGN screening scale. The model was verified internally. A prospective cohort of patients was randomly divided by envelope method into those assessed by the GGN screening scale and those assessed via CT values. The diagnostic efficiencies were compared to allow external verification of the model. Result A total of 223 patients with 225 GGNs were recruited into the retrospective cohort between January 2021 and December 2022. Multivariable analysis showed sex, diameter, air bronchogram, and vessel convergence sign to be independent factors for prediction of noninvasive and invasive GGNs. Internal verification showed the model had a sensitivity of 70.7% and specificity of 75.0% with the Youden index at 0.457 and area under the curve (AUC) of 0.793 (95% CI: 0.734–0.852). Calibration curves indicated good internal stability (p = 0.357). Between January 2023 and March 2023, 147 patients with 148 GGNs were recruited into the prospective cohort. External verification showed the model had a sensitivity of 92.4% and specificity of 40.0% with the Youden index at 0.324 and AUC of 0.678 (95% CI: 0.509–0.847). Calibration curves indicated good external stability (p = 0.088). The scale was shown to have a sensitivity of 75.00%, specificity of 37.50%, positive predictive value of 91.53%, negative predictive value of 14.29%, and accuracy of 71.25%. Conclusion The GGN screening scale has high sensitivity and accuracy, making it suitable for diagnosis of GGNs.

This study aimed at deepening into the internal structure's analysis of the Argentinean Scale of Suicidality, Youth version—ASSY-I Screening—. The 6-factor model obtained in a previous exploratory factor study was tested via confirmatory factor analysis, and further compared with an alternative second-order model. The factorial invariance was examined using the self-perceived gender as a splitting variable. The factors' internal consistency was analysed in the whole sample and in two subsamples comprising exclusively men and women. The scores' stability-reliability was examined using a test-retest analysis. The sample was composed of 632 students with ages between 13 and 21 years old (M = 17.88, SD = 2.16; 64.08% women, 35.92% men). Results showed evidence favouring the robustness of the ASSY-I model as well as the likelihood of generalising it across groups in the local population of young students. Findings are discussed regarding further uses of the scale in the professional field. [ABSTRACT FROM AUTHOR]

Introduction: Depression is often difficult to detect in long-term care (LTC) patients with major neurocognitive disorders (MNCD), and an observer-rated screening scale could facilitate assessments. This study aimed to establish the external validity and reliability of the Nursing Homes Short Depression Inventory (NH-SDI) in LTC patients with MNCD and to compare its estimates to the Cornell Scale for Depression in Dementia (CSDD), the most used scale for depression in MNCD. Methods: A focus discussion group of experts assessed the content validity of the NH-SDI. Then, a convenience sample of 93 LTC patients with MNCD was observer-rated by trained nurses with the NH-SDI and CSDD. For 57 patients, a medical assessment of depression was obtained, and screening accuracy estimates were generated. Results: The prevalence of depression was 8.8% as per reference standard. NH-SDI's content validity was judged acceptable with minor item wording modifications and specifications. The NH-SDI (cut-off ≥3) achieved 100% (95% confidence interval [CI]: 46–100%) sensitivity, 83% (95% CI: 69–91%) specificity, and 36% (95% CI: 14–64%) positive predictive value (PPV). The CSDD (cut-off ≥3) achieved 100% (95% CI: 46–100%) sensitivity, 75% (95% CI: 61–86%) specificity, and 28% (95% CI: 11–54%) PPV. No significant differences in areas under the receiver operating characteristic curve were found between scales. The NH-SDI and CSDD were highly correlated (rs = 0.913; p < 0.001) and reliable (ICC = 0.77; p < 0.001). Conclusion: The NH-SDI appears valid and reliable in LTC patients with MNCD and quicker than the CSDD to rule out depression in a busy or short-staffed setting. [ABSTRACT FROM AUTHOR]

Introduction: Depression is often under-detected in long-term care (LTC) patients with major neurocognitive disorders (MNCD) and is associated with important morbidity, mortality, and costs. Observer-rated outcome measures (ObsROMs) could help resolve this problematic; however, evidence on their accuracy is scattered in the literature. This systematic review aimed at summarizing this evidence. Methods: A literature search was conducted in 7 databases using keywords, MeSHs, and bibliographic searches. We included studies published before January 2022 and reporting on the accuracy of a depression ObsROM used in LTC patients with MNCD. Data extraction, analysis, synthesis, and study methodological quality assessments were done by two authors, and discrepancies were resolved by consensus. Results: Among 9,660 articles retrieved, 8 studies reporting on 11 depression measures were included. Scales were classified as patient-reported outcome measures used as Obs-ROMs or true ObsROMs. Among the first category, the Cornell Scale for Depression in Dementia (CSDD) and the Montgomery-Asberg Depression Rating Scale (MADRS) performed best (area under the curve [AUC]: 0.73–0.87), although both presented with low positive predictive values and high negative predictive values. Among the second category, the Nursing Homes Short Depression Inventory (NH-SDI) performed best, with an AUC of 0.93 and ≥85% sensitivity, specificity, and predictive values. Conclusion: The CSDD and MADRS may be useful to rule out depression in LTC patients with MNCD, whereas the NH-SDI may be useful to rule in and out depression within this same population. Before recommending their use, adequately powered studies to further examine their accuracy in different contexts are necessary. [ABSTRACT FROM AUTHOR]

COVID-19 is a huge global challenge, on which numerous people and resources all over the world have been focused over the last three years to slow down its spread. A screening scale for the early detection of COVID-19 cases is highly important to diagnose patients before they transmit the disease to others. Using the data collected from Sept. 2020 to Nov. 2020 from 12,123 employees of a company including a weekly self-report questionnaire and information on their demographics, chronic disease, work, and commute, we developed a short, optimized screening scale for the early detection of COVID-19. To develop the scale, we have modified an interpretable machine learning (ML) method which uses integer programming​ techniques, and consequently reduced its computational cost significantly and made it robust to erroneous data and protective of sensitive information without loss of performance. The developed scale is highly interpretable and easily scored to the extent that it does not require a computer or even a calculator. However, we showed that the operating characteristics of the screening scale on the test dataset are excellent (area under the ROC curve = 82% and area under the Precision–Recall curve = 27%) and comparable to other well-known ML models such as penalized logistic regression and random forest when predicting positive PCR test results within 7 days from the date the self-report questionnaire was filled out. From Dec. 2020 to Nov. 2021, the developed scale has been used in the company weekly to monitor and assess its employees. Although different variants of COVID-19 have emerged since the development of the scale, the results show that the scale has performed well in the early detection of COVID-19, which helped the company take appropriate actions to slow down the spread of COVID-19 among its employees successfully. We expect our findings in this paper to help researchers have a better understanding of COVID-19 and control the spread of the disease. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop and validate a gaming disorder screening scale based on the ICD-11 criteria for Gaming disorder. 12 main items and 5 supplementary items were created to measure functional impairment and the three basic criteria of ICD-11 for gaming disorder. A total of two samples were used to select items and analyze psychometric properties. The first sample was collected from 252 adolescents and adults who had regular game use experience, some of which corresponded to Repeated measurement data were collected after 6 weeks on 202 subjects. The second sample data was collected from 132 people in the game disorder subclinical group. In both samples, a single-factor model best explained the data. A total of 9 questions were finally selected among the main questions, and the questions showed high factor loadings and reliability. In addition, as a result of evaluating validity through correlation analysis with factors related to gaming disorder, all showed excellent official validity and convergent validity. As a result of the ROC analysis, the total score was determined as 10 points for the criteria for the classification of game disorder risk groups in the finalized selection tool. [ABSTRACT FROM AUTHOR]

• The Early Psychotic Symptoms screening scale (EPSy) is a new scale to assess the prodromes of psychotic symptoms in children aged 4 to 13 years. • Symptoms of mistrust are to be an important and very early indicator of psychosis. • Symptoms of disorganization gain a similar status as mistrust as children grow older. • Symptoms of mistrust and disorganization should indicators for follow-up of children who could be qualified as ultra-high risk for psychosis. This study aimed to develop a new scale, the Early Psychotic Symptoms screening scale (EPSy), to assess the prodromes of psychotic symptoms in children aged 4 to 13 years. Two versions were proposed: one to assess the child's current behavior and one to assess the child's behavior when he/she was 2 years old. The second aim of this study was to investigate the presence of these symptoms at the age of 2 years and their evolution up to the child's current age. The analysis of EPSy identified three main factors, namely mistrust/paranoia, perceptual aberrations/hallucinations and disorganized symptoms. It has good psychometric properties. Data also shows that, independently of the participant's age, the total score on the 2-years-old version predicts the total score on the current-age version, and this is also the case for each individual factor. Finally, it is of clinical interest since it makes it possible to describe symptomatology both at age 2 and at the child's present age depending on the group to which the children are assigned (control children, psychotic children, non-psychotic children). [ABSTRACT FROM AUTHOR]