The AEQ-S: A short version of the Achievement Emotions Questionnaire

The Achievement Emotions Questionnaire (AEQ) is a well-established instrument for measuring achievement emotions in educational research and beyond. Its popularity rests on the coverage of the component structure of various achievement emotions across different academic settings. However, this broad conceptual scope requires the administration of 6 to 12 items per scale (Mdn = 10), which limits the applicability of the AEQ in empirical studies that necessitate brief administration times. We therefore developed the AEQ-S, a short version of the AEQ, with only 4 items per scale that nevertheless maintain the conceptual scope of the instrument. We validated the AEQ-S based on a reanalysis of Pekrun, Goetz, Frenzel, Barchfeld, and Perry's (2011) dataset (N = 389 university students) and by administering them to a new and independent validation sample (N = 471 university students). Despite their brevity, the AEQ-S scales achieved satisfactory reliability and correlated substantially with the original AEQ scales. Moreover, structural relationships and intercorrelations between the scales and their relations with external measures of antecedents and outcomes of achievement emotions were highly similar for the AEQ-S and AEQ scales. These findings suggest that the AEQ-S is a suitable substitute for the AEQ when administration time is limited.

Psychometric Evaluation of the Malay Version of the Family Adaptability and Cohesion Evaluation Scale III for Malaysian Adolescents

The Family Adaptability and Cohesion Scale III (FACES-III) has been widely used to measure an individual’s family functioning in terms of cohesion and adaptability. In Malaysia, the FACES-III has been translated into the Malay language for the community, but its psychometric properties in this context remain unknown. Thus, the purpose of this research is to examine the psychometric properties of the Malay version of the FACES-III in 852 adolescents attending secondary schools in Kuala Lumpur, Malaysia. Data were randomly split into two halves: the exploration sample and the validation sample. Exploratory factor analysis was conducted on the exploration sample and a two-factor model was discovered after removing nine items that showed low factor loading. Then, confirmatory factor analysis was conducted on the validation sample to compare the one-factor models, two-factor models, and three-factor models. Results showed that the 11-item two-factor model (FACES-III-M-SF) was superior to the other competing models. Both the exploratory and confirmatory factor analyses replicated the two-factor structure of the original version of FACES-III. The reliability of the overall scale was consistently good, but the subscale results were mixed. This suggests that researchers should use the overall score, but not the subscale scores, in analyses.

Structure of Negative Symptoms in Schizophrenia: An Unresolved Issue

Background: Negative symptoms are core features of schizophrenia and very challenging to be treated. Identification of their structure is crucial to provide a better treatment. Increasing evidence supports the superiority of a five-factor model (alogia, blunted affect, anhedonia, avolition, and asociality as defined by the NMIH-MATRICS Consensus); however, previous data primarily used the Brief Negative Symptoms Scale (BNSS). This study, including a calibration and a cross-validation sample (n = 268 and 257, respectively) of participants with schizophrenia, used the Clinical Assessment Interview for Negative Symptoms (CAINS) to explore the latent structure of negative symptoms and to test theoretical and data-driven (from this study) models of negative symptoms.Methods: Exploratory factor analysis (EFA) was carried out to investigate the structure of negative symptoms based on the CAINS. Confirmatory factor analysis (CFA) tested in a cross-validation sample four competing theoretical (one-factor, two-factor, five-factor, and hierarchical factor) models and two EFA-derived models.Result: None of the theoretical models was confirmed with the CFA. A CAINS-rated model from EFA consisting of five factors (expression, motivation for recreational activities, social activities, vocational, and close/intimate relationships) was an excellent fit to the data (comparative fix index = 0.97, Tucker–Lewis index = 0.96, and root mean square error of approximation = 0.07).Conclusions: This study cannot support recent data on the superiority of the five-factor model defined by the NMIH-MATRICS consensus and suggests that an alternative model might be a better fit. More research to confirm the structure of negative symptoms in schizophrenia, and careful methodological consideration, should be warranted before a definitive model can put forward and shape diagnosis and treatment of schizophrenia.

Sex-Specific 25-Hydroxyvitamin D Threshold Concentrations for Functional Outcomes in Older Adults

25-Hydroxyvitamin D [25(OH)D] has extra-skeletal effects, but it is not known whether the minimum sufficient serum levels for such targets, like muscle, differ from those for bone health (typically recommended at 20 or 30 ng/dL). Therefore, we derived and validated sex-specific thresholds for serum 25(OH)D predictive of poor physical function using 5 cohorts comprising 16,388 community-dwelling older adults (60.9% women). Using a cohort-stratified random two-thirds sample, we found incident slow gait was best discriminated by 25(OH)D<24.0 versus 25(OH)D>=24.0 ng/mL among women (Relative Risk=1.29; 95% CI 1.10-1.50) and 25(OH)D<21.0 versus 25(OH)D >=21.0 ng/mL among men (RR=1.43; 95% CI 1.01-2.02). Estimates from the remaining one-third validation sample were similar. Empirically identified and validated sex-specific 25(OH)D thresholds from multiple well-characterized cohorts of older adults may yield more biologically meaningful definitions in important sub-populations. Such thresholds may serve as candidate reference concentrations or inform design of vitamin D intervention trials in older adults.

Derivation and validation of a type 2 diabetes treatment selection algorithm for SGLT2-inhibitor and DPP4-inhibitor therapies based on glucose-lowering efficacy: cohort study using trial and routine clinical data

Objective: To establish whether clinical patient characteristics routinely measured in primary care can identify people with differing short-term benefits and risks for SGLT2-inhibitor and DPP4-inhibitor therapies, and to derive and validate a treatment selection algorithm to identify the likely optimal therapy for individual patients. Design: Prospective cohort study. Setting: Routine clinical data from United Kingdom general practice (Clinical Practice Research Datalink [CPRD]), and individual-level clinical trial data from 14 multi-country trials of SGLT2-inhibitor and DPP4-inhibitor therapies. Participants: 26,877 new users of SGLT2-inhibitor and DPP4-inhibitor therapy in CPRD over 2013-2019, and 10,414 participants randomised to SGLT2-inhibitor or DPP4-inhibitor therapy in 14 clinical trials, including 3 head-to-head trials of the two therapies (n=2,499). Main outcome measures: The primary outcome was achieved HbA1c 6 months after initiating therapy. Clinical features associated with differential HbA1c outcomes with SGLT2-inhibitor and DPP4-inhibitor therapies were identified in routine clinical data, with associations then tested in trial data. A multivariable treatment selection algorithm to predict differential HbA1c outcomes was developed in a CPRD derivation cohort (n=14,069), with validation in a CPRD validation cohort (n=9,376) and the head-to-head trials. In CPRD, we further explored the relationship between model predictions and secondary outcomes of weight loss and treatment discontinuation. Results: The final treatment selection algorithm included HbA1c, eGFR, ALT, age, and BMI, which were identified as predictors of differential HbA1c outcomes with SGLT2-inhibitor and DPP4-inhibitor therapies using both routine and trial data. In validation cohorts, patient strata predicted to have a ≥5 mmol/mol HbA1c reduction with SGLT2-inhibitor therapy compared with DPP4-inhibitor therapy (38.8% of CPRD validation sample) had an observed greater reduction of 8.8 mmol/mol [95%CI 7.8-9.8] in the CPRD validation sample, a 5.8 mmol/mol (95%CI 3.9-7.7) greater reduction in the Cantata D/D2 trials, and a 6.6 mmol/mol [95%CI 2.2-11.0]) greater reduction in the BI1245.20 trial. In CPRD, there was a greater weight reduction with SGLT2-inhibitor therapy regardless of predicted glycaemic benefit. Strata predicted to have greater reduction in HbA1c on SGLT2-inhibitor therapy had a similar risk of discontinuation as on DPP4-inhibitor therapy. In contrast, strata predicted to have greater reduction in HbA1c with DPP4-inhibitor therapy were half as likely to discontinue DPP4-inhibitor therapy than SGLT2-inhibitor therapy. Conclusions: Routinely measured clinical features are robustly associated with differential glycaemic responses to SGLT2-inhibitor and DPP4-inhibitor therapies. Combining features into a treatment selection algorithm can inform clinical decisions concerning optimal type 2 diabetes treatment choices.

The differential diagnosis of IgG4-related disease based on machine learning

Introduction: To eliminate the disparity and maldistribution of physicians and medical specialty services, the development of diagnostic support for rare diseases using artificial intelligence is being promoted. Immunoglobulin G4 (IgG4)-related disease (IgG4-RD) is a rare disorder often requiring special knowledge and experience to diagnose. In this study, we investigated the possibility of differential diagnosis of IgG4-RD based on basic patient characteristics and blood test findings using machine learning. Methods Six-hundred and two patients with IgG4-RD and 212 patients with non-IgG4-RD that needed to be differentiated who visited the participating institutions were included in the study. Ten percent of the subjects were randomly excluded as a validation sample. Among the remaining cases, 80% were used as training samples, and the remaining 20% were used as test samples. Finally, validation was performed on the validation sample. The analysis was performed using a decision tree and a random forest model. Furthermore, a comparison was made between conditions with and without the serum IgG4 concentration. Accuracy was evaluated using the area under the receiver-operating characteristic (AUROC) curve. Results In diagnosing IgG4-RD, AUROC curve values of the decision tree and the random forest method were 0.905 and 0.970, respectively, when serum IgG4 levels were included in the analysis. Excluding serum IgG4 levels, the AUROC curve value of the analysis by the random forest method was 0.919. Conclusion Based on machine learning in a multicenter collaboration, with or without serum IgG4 data, basic patient characteristics and blood test findings alone were sufficient to differentiate IgG4-RD from non-IgG4-RD.

Age- and Sex-Adjusted Reference Intervals in Tear Cytokine Levels in Healthy Subjects

Alterations in tear cytokine levels have been associated with various ocular disorders as compared to those in healthy subjects. However, age and sex are not always considered in these comparisons. In this study we aimed to establish age and sex reference intervals (RIs) for tear cytokine levels in healthy people. Tear samples were taken from 75 males and 82 females, aged 18–88 years, and tear cytokine levels were determined. Age- and sex-adjusted RIs for epidermal growth factor (EGF), fractalkine, interleukin (IL)-1 receptor antagonist (RA), IL-7, IL-8, interferon inducible protein (IP)-10, monocyte chemotactic protein (MCP)-1, and vascular endothelial growth factor (VEGF) tear cytokine levels in a healthy sample were established using generalized additive for location, scale and shape (GAMLSS) models. RIs were tested in two external samples: a validation sample of 40 individuals with normal results at four Dry Eye Disease (DED) clinical diagnostic tests (OSDI, T-BUT, corneal staining and Schirmer test); and a utility sample of 13 severe DED cases. IL-1RA, IL-8, IP-10, and MCP-1 levels showed a positive association with age, while EGF was negatively correlated. IL-7 concentration increased up to 40 years and again after 70 years, observing a quasi-linear decrease between them. For VEGF, higher levels were observed in the middle-aged range. Regarding sex-influence, fractalkine tear levels were higher in men, whereas those of IL-7, IL-8, and IP-10 were higher in women. Using the estimated age- and sex-adjusted RIs, more than 92% of the validation sample was correctly classified, and 100% of the severe DED patients in the utility sample had concentrations outside the RIs in at least two of the cytokines evaluated.

Development and Validation of Nomograms to Predict Operative Link for Gastritis Assessment Any-Stage and Stages III–IV in the Chinese High-Risk Gastric Cancer Population

Purpose: It is very essential to diagnose gastric atrophy in the area with high prevalence of gastric cancer. Operative link for gastritis assessment (OLGA) was developed to detect the severity of gastric atrophy. The aim of this study was to develop and validate nomograms for predicting OLGA any-stage and stages III–IV in the Chinese high-risk gastric cancer population.Methods: We retrospectively analyzed 7,945 participants obtained by a multicenter cross-sectional study. We randomly selected 55% individuals (4,370 participants, training cohort) to analyze and generate the prediction models and validated the models on the remaining individuals (3,575 participants, validation cohort). A multivariate logistic regression model was used to select variables in the training cohort. The corresponding nomograms were developed to predict OLGA any-stage and stages III–IV, respectively. The area under the receiver operating characteristic curves and the GiViTI calibration belts were used to estimate the discrimination and calibration of the prediction models.Results: There were 1,226 (28.05%) participants in the training sample and 970 (27.13%) in the validation sample who were diagnosed with gastric atrophy. The nomogram predicting OLGA any-stage had an area under the curve (AUC) of 0.610 for the training sample and 0.615 for the validation sample, with favorable calibrations in the overall population. Similarly, the nomogram predicting OLGA stages III–IV had an AUC of 0.702 and 0.714 for the training and validation samples, respectively, with favorable calibrations in the overall population.Conclusions: The prediction model can early identify the occurrence of gastric atrophy and the severity stage of gastric atrophy to some extent.

The Frontal Behavioral Battery: A Measure of Frontal Lobe Symptoms in Brain Aging and Neurodegenerative Disease

Background: Approximately 90%of persons living with dementia experience behavioral symptoms, including frontal lobe features involving motivation, planning, social behavior, language, personality, mood, swallowing, and gait. Objective: We conducted a two-stage study with a development sample (n = 586) and validation sample (n = 274) to evaluate a brief informant-rated measure of non-cognitive features of frontal lobe dysfunction: the Frontal Behavioral Battery (FBB). Methods: In the development sample, internal consistency, principal factor analysis, and correlations between the FBB and outcomes were evaluated. In the validation sample, we examined (a) FBB scores by diagnosis, (b) known-group validity by demographics, subjective complaints, and dementia staging, and (c) correlation between FBB and MRI volumes. Receiver operator characteristic curves assessed the ability of the FBB to discriminate individuals with frontal lobe features due to a neurodegenerative disease. Results: The FBB characterized 11 distinct frontal lobe features. Individuals with dementia with Lewy bodies and frontotemporal degeneration had the greatest number of frontal lobe features. Premorbid personality traits of extroversion, agreeableness, and openness were associated with fewer frontal lobe behavioral symptoms, while subjective cognitive complaints were associated with greater symptoms. The FBB provided very good discrimination between individuals with and without cognitive impairment (diagnostic odds ratio: 13.1) and between individuals with and without prominent frontal lobe symptoms (diagnostic odds ratio: 84.8). Conclusion: The FBB may serve as an effective and efficient method to assess the presence of non-cognitive symptoms associated with frontal lobe dysfunction, but in a brief fashion that could facilitate its use in clinical care and research.

The Development and Validation of a Short Form of the Forbearance Scale

The Forbearance Scale (FS) is a 16-item self-report measure of forbearance. In this study, we examined the psychometric properties of the FS subscale and composite scores and developed a 9-item short form of the measure (FS-SF 9). A sample of 1,137 participants was drawn from community, NGO, and college settings. The sample was split into a derivation sample (n = 567) and a validation sample (n = 570). Exploratory factor analyses of the derivation sample data were used to select short-form items. Using the validation sample, confirmatory factor analyses were used to assess fit for proposed item-to-factor assignments. The results of the confirmatory factor analyses supported that the FS-SF 9 had a theoretically congruent factor structure and that all the subscale and composite scores displayed high internal consistency. Correlations with scores from established measures of a lack of forgiveness and emotion regulation also supported the validity of the FS-SF 9. Our data suggest that the FS-SF 9 subscales and composite score retained the psychometric strengths of their longer FS counterparts. Overall, the short form of the FS provides a brief assessment of the construct measured by the full form. Theoretical and practical applications are discussed.