Fit Statistics Research Articles

Measuring a family sense of coherence: a rasch-based study extending dyadic data analyses

BackgroundFamily sense of coherence (FSOC) seems to reduce distress in the family and promote the well-being of the family. Therefore, getting accurate measurements for families with long-term illnesses is of particular interest. This study explores dyadic data analysis from the dyadic- and single-informant perspectives, and the measurement properties of the FSOC-S12 according to the Rasch model.MethodsRacked and stacked data from 151 dyads were analyzed according to the polytomous Rasch model.ResultsNotably, both the dyadic- and single-informant perspectives (i.e., racked and stacked data set-ups) showed measurement properties with minor deviations from the Rasch model according to fit statistics. However, most items had disordered thresholds and some problems with local dependency. Item hierarchies were similar in both set-ups and there was no differential item functioning (DIF) by role from the dyadic informant perspective. Four items showed DIF by informant role in the single-informant perspective.ConclusionsOur approach to handling dyadic data has shown both strengths and limitations in the evaluation of FSOC-S12, and the understanding of FSOC as a construct from the family’s view of the family’s ability as a whole (dyadic-informant perspective) and patient’s and family member’s separate views of the family’s ability as a whole (single-informant perspective).

Soil-aggregate-cement mixtures for base pavement layers: A strength and stiffness characterization

Pavements subjected to high volume load require a robust structure. In Brazil, cemented base layer is usually used instead of asphalt concrete thick layer due to cost issues. Soil-aggregate-cement (SAC) mixture is an alternative in pavement application, but the lack of design and dosage protocols hinders the understanding of the parameters influencing its mechanical behavior. This study presents a mechanical characterization of SAC mixtures in terms of strength (UCS and ITS) and stiffness (Mr), focusing on integrating the needs of pavement design to dosage purposes. For this, different SAC mixtures are produced, varying the soil-aggregate proportion (30:70 and 20:80) and cement content (3, 5 and 7 %). Mechanical properties were measured on cured mixtures at 0, 7 and 28 days and compared to structural responses of hypothetical pavements computed by mechanistic analysis. The results indicate a potential for using SAC mixtures in base layers due to their higher strength and stiffness. Predictive models relating ITS x UCS, Mr x UCS, and Mr x ITS with good statistical fit were proposed to assist researchers and engineers in selecting mixtures more adequate and durable for pavement applications, based on the concept of limiting the tensile stress acting on the cemented base course.

Analyzing the impact of non-Newtonian nanofluid flow on pollutant discharge concentration in wastewater management using an artificial computing approach

Wastewater discharge is important in numerous areas of industries and in governance of the environmental sectors. Controlling and monitoring water pollution are essential for protecting the availability of water and upholding standards of sustainability. Thus, in the current study, the effects of pollutant discharge concentration (PDC) are considered while analyzing the flow of non-Newtonian nanofluids (NNNF) through the permeable Riga surface subject to heat radiation. Walter’s B fluid (WBF) and second-grade fluids (SGFs), two distinct types of NNNF, have been investigated. The fluid flow is expressed as a system of PDEs, which are simplified into lower order by employing similarity approach. These equations (ODEs) are solved using the Levenberg Marquardt back-propagation optimization algorithm (LMBOA) of the artificial neural network (ANN). The Matlab package “bvp4c” is used for generating the dataset in order to validate the results of the ANN-LMBOA. The dataset was developed for various flow scenarios, as well as ANN evaluation and validation. The accuracy of the ANN-LMBOA model is estimated though numerous statistical tools, i.e., histogram, regression measures, curve fitting, performance plots, and validation tables. The numerical outcomes of bvp4c package are also compared to the published literature. Which show best accuracy and resemblance with each other for the limiting case. The targeted date absolute error is accomplished within the range of 10–4-10–5 which confirms the outstanding accuracy of ANN-LMBOA. It is concluded form error histograms (EHs) that the EHs values for case 1–4 is lie about 3·6×10-7, 7·83×10-9, -4.7×10-8 and -2·9×10-6 respectively.

Material Hardship, Forced Displacement, and Negative Health Outcomes Among Unhoused People Who Use Drugs in Los Angeles, California and Denver, Colorado: A Latent Class Analysis.

Homelessness is a growing concern in the United States, especially among people who use drugs (PWUD). The degree of material hardship among this population may be linked to worse health outcomes. PWUD experiencing homelessness in urban areas are increasingly subjected to policies and social treatment, such as forced displacement, which may worsen material hardship. It is critical to describe hardship among PWUD and examine if it is linked to health outcomes. Data were collected as part of a prospective cohort study of PWUD in Los Angeles, California and Denver, Colorado (n = 476). Analysis sample size was smaller (N = 395) after selecting for people experiencing homelessness and for whom data were complete. Five indicators assessing hardship (difficulty finding food, clothing, restrooms, places to wash/shower, and shelter) in the past three months were obtained from participants at baseline and were used in latent class analysis (LCA). We chose a base latent class model after examination of global fit statistics. We then built three auxiliary models using the three-step Bolck-Croon-Hagenaars (BCH) method to test the relationship of latent class membership to several hypothesized social and health variables in this same three month time period. Fit statistics, minimum classification probabilities, and ease of interpretation indicated a three-class solution for level of material difficulty. We termed these classes "High Difficulty" (n = 82), "Mixed Difficulty" (n = 215), and "Low Difficulty" (n = 98). Average classification probabilities indicated good class separability. "High Difficulty" participants had high probabilities of usually having difficulty accessing all five resources. "Mixed Difficulty" participants indicated a range of difficulty accessing all resources, with restrooms and bathing facilities being the most difficult. "Low Difficulty" participants were defined by high probabilities of never having access difficulty. In auxiliary analyses, there were significant (p < 0.05) differences in experiences of displacement, opioid withdrawal symptoms, nonfatal overdose, and violent victimization between classes. This LCA indicates that among PWUD experiencing homelessness there exist distinct differences in resource access and material hardship, and that these differences are linked with political, social, substance use, and other health outcomes. We add to the literature on the relationship between poverty and health among PWUD. Policies which increase difficulty accessing necessary material resources may negatively impact health in this population.

Home Pulse Rate Before and During Antihypertensive Treatment and Mortality Risk in Hypertensive Patients: A Post Hoc Analysis of the HOMED-BP Study.

Although a high pulse rate assessed in the clinic office setting has been associated with an increased risk of cardiovascular disease and mortality, there are few studies assessing the prognostic ability of out-of-office pulse rate, particularly self-measured home pulse rate. We investigated the prognostic ability of home pulse rate in 3022 patients with mild-to-moderate hypertension. During a median follow-up of 7.3 years, 72 patients died and 50 had major adverse cardiovascular events. For each 1 SD increase in pulse rate before treatment (9.4 beats per minute), the adjusted hazard ratio for all-cause mortality was 1.52 (95% CI, 1.24-1.92). For each 1 SD increase in pulse rate during the follow-up period (9.9 beats per minute), the adjusted hazard ratio was 1.70 (95% CI, 1.39-2.08). However, pulse rate was not significantly associated with major adverse cardiovascular events. When both home pulse rate and office pulse rate before treatment were included in a Cox model, only the home pulse rate significantly predicted all-cause mortality (P ≤0.019). Excluding the home pulse rate from the model led to a significant deterioration of the model fit statistic (P ≤0.020). The optimal cut-off values of home pulse rate in predicting all-cause mortality, determined by Youden's index from a receiver operator characteristic analysis, were 67.8 beats per minute at baseline and 66.4 beats per minute during follow-up. In patients with mild-to-moderate hypertension, the pulse rate measured at home, both before and during antihypertensive treatment, was associated with mortality risk and has superior prognostic ability compared with office pulse rate. The accuracy of risk stratification may be improved by using a home pulse rate, which can be self-measured easily and frequently at home. URL: https://www.umin.ac.jp/ctr; Unique identifier: C000000137.

Evaluation of theoretical models for anisotropic effective thermal conductivity in continuous fiber-reinforced thermoplastic laminates

Purpose Continuous fiber-reinforced thermoplastic composites are a class of materials highly valuable for structural applications and modeling of heat transfer within them is critical to the design of their processing methods. However, the fiber reinforcement leads to highly anisotropic thermal conduction. Among a variety of methods to account for anisotropic thermal conductivity, continuum models with effective media approximation thermal conductivity are computationally efficient and require minimal data to begin modeling a specific composite material. The purpose of this study is to evalute the utility of these models. Design/methodology/approach In this work, six potential effective media approximation models are evaluated against experimental heating data. Thick (&gt;25 mm) glass fiber-reinforced polyethylene terephthalate glycol (PET-G) specimens with 40% fiber volume fraction were heated with embedded resistance heating to produce validation and testing data sets. A two-dimensional finite-difference solver was implemented using each of the six effective media approximation models. The accuracy of each model is compared. Findings The model developed by Cheng and Vachon was found to predict the experimental results most accurately. Fit statistics were similar in the testing and validation data sets. This model is recommended for simulation of transient heating in continuous fiber-reinforced thermoplastic composites with low-to-moderate fiber volume fractions. Originality/value There are a wide variety of mathematical models for effective media approximation thermal conductivity, though very few have been applied to continuous fiber-reinforced thermoplastic composites. This work shows that the simplest methods based on rules of mixtures are well outperformed by more modern and complex models, and should be incorporated for accurate prediction of heating during thermal processing of fiber-reinforced thermoplastic composites.

Modelling and Forecasting Currency Exchange Rates Using Hybrid ARIMA and ANN Models in High-Frequency Data

Introduction: Exchange rates are central to the economic as well as the financial systems and structures because they determine trade, investment and policy. This study focuses on the statistical analysis and forecasting of exchange rates for the Kenyan Shilling (KES) against regional currencies: The currencies include; Burundian Franc (BIF), Rwandan Franc (RWF), Tanzanian Shilling (TSHS) and Ugandan Shilling (USHS). Methods: Significance tests and models of time series were used in the analysis with regard to trend and volatility of exchange rates. Descriptive measures which include mean, median, standard deviation and range were calculated for each of the currency pairs. Furthermore, to determine cycles and rate predictions, a more complex ARIMA (1,1,0) with drift model was applied. Various statistics namely AIC, AIC c and BIC were used to assess model performance. Results: It was seen that the mean rate of KES/USHS was the highest at 27.0 and KES/RWF was the least at 8.99. The standard deviations suggested moderate fluctuation in the pairs with the KES/USHS exchanging at 2.26. The results of the ARIMA analysis were highly significant for coefficients (AR1 = 0.5359, drift = 0.0412), and feasible for Fit statistics (AIC = -123.57, BIC = -116.07). It also pointed out that there were high positive correlations between all the currency pairs analyzed; the values being above 0.98. Discussions: The results also confirm narrow fixed relationships between KES and the regional currencies with fluctuation in KES/USHS. The applied ARIMA model allows for capturing the dynamics of exchange rate and provides accurate forecast. The result of this study can be useful for policymakers and financial analyst in order to comprehend behaviors of regional currencies.

Refining holographic models of the quark-gluon plasma

We investigate quark-gluon plasma at nonzero density by using two holographic models for QCD based on either brane or Einstein-Maxwell-dilaton actions. We determine the parameters of these models through a systematic statistical fitting procedure to lattice QCD data, which encompasses the equation of state (through the entropy density) and the two lowest baryon number susceptibilities. The predictions from the two models for the higher-order susceptibilities and the equation of state at nonzero density are strikingly similar. In particular, both models suggest the presence of a critical point on the (μB,T)-phase diagram near μB/T≈6. The results for the equation of state are in agreement with lattice data for higher-order susceptibilities and experimental data for the cumulants of the net-proton number from the beam energy scan program at the Relativistic Heavy-Ion Collider. Published by the American Physical Society 2024

Using Differential Scanning Calorimetry to Measure the Energetic Properties of Residual Sludge and Catalysts from the Textile, Tannery, and Galvanic Industries

This research delved into the energetic properties of catalysts synthesized from residual sludge from the textile, galvanic, and tannery industries. The experimental process consisted of an initial heat treatment to activate their catalytic properties and a thermal analysis employing differential scanning calorimetry (DSC). This technique permitted the investigation of the materials’ thermal behavior as a function of temperature, ranging from 142 to 550 °C, effectively controlling the heating rates and pressure conditions. The data gathered were the input for constructing specific heat models through polynomial regression employing the least squares method. These models were subsequently used to estimate variations in the enthalpy and entropy for both the sludge and catalysts through integration. Third-degree polynomials primarily characterized the specific heat models that accurately represented the samples’ thermal behavior, considering variations in their physicochemical properties that influenced it. The catalysts derived from residual sludge from the textile industry exhibited the models with the most robust statistical fit. Concurrently, the catalysts from the galvanic industry displayed noteworthy similarities with the bibliographic data across various temperature points. The mathematical models determined the specific heat (Cp) as a function of temperature, which, in turn, was used to estimate the enthalpy and entropy variations in the sludge and catalysts under study. The highest enthalpy value corresponded to the sludge and catalyst obtained from the tannery industry, with a Cp of 5.60 J/g-K at 603 K and 2.45 J/g-K at 445.6 K. Finally, the third-degree polynomials showed the best mathematical models since (1) they considered the variations in the physicochemical properties that intervened in the behavior of Cp as a function of temperature; (2) they presented a better statistical fit; and (3) they showed consistency with the existing information in the literature for the textile industry and the galvanic industries.

The Holland Sleep Disorders Questionnaire: Factorial structure and measurement invariance in a psychiatric sample relative to the general population.

Although common, sleep disorders often remain undiagnosed in psychiatric patients. A screening instrument, like the Holland Sleep Disorders Questionnaire (HSDQ) could improve this. Previous work indicated a 6-factor structure for the HSDQ, but this hasn't been investigated in psychiatric patients. HSDQ data was collected in a psychiatric-outpatient sample (n=1082) and general-population sample (n=2089). Internal reliability of the HSDQ was investigated and Confirmatory Factor Analyses (CFA) were used to compare 1-, 6-, and second-order 6-factor models in both samples. Next, multigroup-CFA was used to investigate measurement invariance. Except for one subscale, internal reliability was acceptable in both samples. The 6-factor structure model fitted best in both samples and investigation of measurement invariance showed evidence for equality of the overall factor structure (configural invariance). Addition of equality constraints on factor loadings (metric invariance) and item thresholds (scalar invariance) showed good fit for all fit statistics, except for one. Exploratory analyses identified three items for metric and three different items for scalar invariance explaining this non-invariance. The HSDQ has a 6-factor structure in psychiatric patients, which is comparable to the general population. However, due to the observed non-invariance, users should be cautious with comparing HSDQ scores between psychiatric and general populations.

Asymptotically Correct Person Fit z-Statistics For the Rasch Testlet Model.

A well-known person fit statistic in the item response theory (IRT) literature is the statistic (Drasgow et al. in Br J Math Stat Psychol 38(1):67-86, 1985). Snijders (Psychometrika 66(3):331-342, 2001) derived , which is the asymptotically correct version of when the ability parameter is estimated. However, both statistics and other extensions later developed concern either only the unidimensional IRT models or multidimensional models that require a joint estimate of latent traits across all the dimensions. Considering a marginalized maximum likelihood ability estimator, this paper proposes and , which are extensions of and , respectively, for the Rasch testlet model. The computation of relies on several extensions of the Lord-Wingersky algorithm (1984) that are additional contributions of this paper. Simulation results show that has close-to-nominal Type I error rates and satisfactory power for detecting aberrant responses. For unidimensional models, and reduce to and , respectively, and therefore allows for the evaluation of person fit with a wider range of IRT models. A real data application is presented to show the utility of the proposed statistics for a test with an underlying structure that consists of both the traditional unidimensional component and the Rasch testlet component.

Study on dynamic modulus of polyurethane mixture: Impact factors and prediction models

Polyurethane mixture (PUM) is proposed as an innovative and promising paving material due to its superior engineering performance and environmentally-friendliness. This study aims to experimentally investigate dynamic modulus of PUM at various temperatures, loading frequencies and loading strains. Furthermore, prediction models on its dynamic modulus considering factors of temperature, loading frequence as well as their coupling effect are proposed. The results demonstrate that dynamic modulus of PUM approximately varies between 2000 MPa and 19,000 MPa in temperature range of 15–50°C, loading frequency range of 0.1–25 Hz, and loading strains range of 20–100με. Dynamic modulus of PUM at 15°C is approximately 5.62 times higher than that at 50°C. The value at 20 Hz is approximately 2.76 times higher than that at 0.2 Hz. By implementing the analysis of variance (ANOVA) method, it is explored that temperature and loading frequency yield significant impact on dynamic modulus of PUM while loading strain imposes negligible influence on dynamic modulus of PUM. By statistical fitting analysis, dynamic modulus of PUM is linear with temperature and presents power function with loading frequency. Moreover, by considering coupling effect of temperature and loading frequency, prediction model on dynamic modulus of PUM is proposed and verified through experiments. The outcome of this study facilitates design and applications of PUM at different climate conditions and traffic loads.

Prediction model for elastic modulus of recycled concrete based on properties of recycled coarse aggregate and cementitious materials

The supplementary cementitious materials (SCMs) are commonly used in recycled coarse aggregate (RA) concrete to improve the performance of RA concrete or reduce the amount of cement used. However, the diverse sources and compositions of RA and SCMs pose a challenge for predicting the elastic modulus of concrete containing both RA and SCMs. In this study, the main influencing factors on the elastic modulus of recycled concrete were analyzed, and the statistical significance of the influencing factors on the elastic modulus was quantitatively analyzed using factor analysis techniques, either individually or in interaction. The results of the factor analysis reveled that the content of recycled coarse aggregates and the reactivity index of the cementitious materials have the most significant impact on the elastic modulus, followed by the water absorption of recycled coarse aggregates and the ratio of aggregates to cementitious materials (A/C). Furthermore, the interaction between A/C ratio and the content of recycled coarse aggregates, as well as the interaction between the water absorption and the content of recycled coarse aggregates, were also found to be significant influencing factors on the elastic modulus. A unified prediction model for elastic modulus was proposed by considering the hydraulic and pozzolanic activities of OPC and SCMs, performance and content of recycled coarse aggregate simultaneously, based on a statistical analysis of the experimental database. The statistical fitting results indicted that the elastic modulus can be accurately predicted using proposed model, thus promoting the application of recycled aggregate and green binder in structures.

Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach

This paper presents a method for predicting the energy yield of a photovoltaic (PV) system based on the ARIMA algorithm. We analyze two key time series: the specific yield and the total yield of the PV system. Two ARIMA models are developed for each time series: one selected by the authors and one determined by SPSS. Model performance is evaluated through fit statistics, providing a comprehensive assessment of model accuracy. The residuals’ ACF and PACF are examined to ensure model adequacy, and confidence intervals are calculated for residuals to validate the models. A monthly forecast is then generated for both time series, complete with confidence intervals, to demonstrate the models’ predictive capabilities. The results highlight the effectiveness of ARIMA in forecasting PV energy yields, offering valuable insights for optimizing PV system performance and planning. This study contributes to the field of renewable energy forecasting by demonstrating the applicability of ARIMA models in predicting the monthly performance of photovoltaic systems.

Psychometric testing of the caring for the caregiver survey for healthcare providers

AimThe purpose of this study was to assess the factorial structure and model fit of the Caring for the Caregiver Scale. BackgroundCaregiving is an all-encompassing role that results in negative health consequences. Healthcare providers require specific knowledge and clinical skills to support caregivers with health promotion. There is no instrument to measure healthcare providers' knowledge, attitudes, and practices on assessing and supporting caregivers. The Healthcare Provider Caring for the Caregiver Scale was developed to identify gaps in healthcare providers' knowledge and clinical practices and help pinpoint areas where interventions may be necessary. MethodsExploratory factor analysis and absolute fit measures were used to evaluate the factorial structure and model fit of the instrument. A multidimensional ordinal Rasch and differential item functioning test was used to fit a three-dimensional partial credit model and ensure the validity of the use of the instrument across subgroups. ResultsA three-factor model was confirmed based on fit statistics and the clustering of the inter-item correlations. The composite measure across 30 items demonstrated strong internal consistency and excellent reliability. Item fit indices estimated from the Rasch model indicated an overall good fit except for two items showing a misfit. ConclusionsThe Caring for the Caregiver Scale demonstrates sufficient factorial structure and model fit making it valuable in evaluating healthcare providers' knowledge, attitudes, and practices with assessing and supporting caregivers. Data collected from this instrument may identify gaps in knowledge and clinical skills and guide intervention development that enhances healthcare providers' ability to deliver quality care to caregivers.

A Heat Sensitivity Scale for People with Parkinson's Disease.

People living with Parkinson's disease (PD) commonly experience heat sensitivity-worsening symptoms and restricted daily activities in heat. This study aimed to develop a scale of heat sensitivity for people with PD. Through a search of the scientific literature and online forums, we developed 41 items relating to experiences of heat for people with PD to assess heat sensitivity. A panel of experts was then consulted to review the scale items critically. After two rounds of review, the scale was refined to 36 items with an overall scale content validity index of 0.89. Via an online survey, 247 people with PD responded to the items. The items were examined with exploratory factor analysis to determine the underlying factors therein. After several iterations, a simple structure was achieved with 29 items loading uniquely onto one of four factors: daily activities, sweating and exercise, heat-related illness, and symptoms and medications. The model had acceptable to excellent fit statistics (root mean square error of approximation = 0.073 [90% confidence interval 0.067-0.081], root mean square of the residuals = 0.03, comparative fit index = 0.93, and Tucker-Lewis index = 0.91), and each factor showed high reliability (Cronbach's α ≥0.89). Factor and total scale scores were significantly higher among those reporting sensitivity to heat and poor health status. This new heat sensitivity scale for people living with PD can enable health professionals and clients to assess the severity and impact of heat sensitivity.

Factor Structure and Measurement Invariance Across Sex of the Sport Concussion Assessment Tool Symptom Inventory.

Describe the factor structure of the 22-symptom Sport Concussion Assessment Tool (SCAT), using confirmatory factor analysis (CFA) for a priori hypothesized symptom domains. Prospective observational study. Collegiate student-athletes with concussion. Symptoms were collected via the SCAT symptom checklist. We created symptom domains based on previous literature, guided by clinical expertise. To determine which symptom grouping best represent the data, we used CFA and compared a single-domain model to 3- and 6-domains. We examined fit statistics to assess relative and absolute model fit. Motivated by differences in the prevalence of some individual symptoms by sex in our study, we also examined model invariance by sex to determine if symptoms were being measured as part of the same underlying construct(s). Among 1160 concussions (male, n = 667; female, n = 493) between 2015 and 2020, all 3 symptom structures seemed to fit the data well, with 3- and 6-domains fitting better than 1-domain. The 6-domain structure fit the data best with the following domains: headache, vestibulo-ocular, sensory, cognitive, sleep, and affective. All 3 structures showed configural and metric invariance by sex. We demonstrate that the SCAT symptom structure is best represented through 6 specific factors; however, the 3-factor model also demonstrated good fit. Key differences between the 3- and 6-domain models may make 1 model more appropriate than the other depending on the research question being addressed. Symptom structures were configurally and metrically invariant by sex, meaning that symptom measures represent symptom domain factors in the same way across sex.

Optimizing steam condenser efficiency: integrating logarithmic spiral search and greedy selection mechanisms in gazelle optimizer for PI controller tuning

Steam condensers are critical for maintaining efficiency and stability in thermal power plants by converting exhaust steam back into water. Controlling the pressure within the condenser is essential for optimal performance and operational safety. This paper presents a dynamic model of a steam condenser, with pressure control achieved through a proportional-integral (PI) controller. The PI controller parameters are optimized using the gazelle optimization algorithm (GOA), which is applied for the first time to this system in the literature. Additionally, novel enhancements, the logarithmic spiral (Ls) search with greedy selection, are integrated into GOA to further enhance control effectiveness. The performance of the optimized PI controller is evaluated using nonlinear simulations, which include statistical analysis and fitness function assessments. To validate the effectiveness of the proposed approach, comparisons are made with other widely used optimization techniques. The results reveal that the GOA notably enhances control accuracy and stability, with the enhanced GOA (Ls-GOA) achieving the most significant improvements. This underscores its potential for enhancing steam condenser efficiency in thermal power plants.

A Simple Clinical Predictive Model for Arthroscopic Mobility of Osteochondritis Dissecans Lesions of the Knee

Background: Osteochondritis dissecans (OCD) of the knee is a focal idiopathic alteration of subchondral bone and/or its precursor with risk for instability and disruption of adjacent cartilage. Treatment options focused on preventing premature osteoarthritis vary depending on multiple patient and lesion characteristics, including lesion mobility. Purpose: To differentiate lesion mobility before arthroscopy using a multivariable model that includes patient demographic characteristics and physical examination findings. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: Demographic, preoperative physical examination, and radiographic data were collected from a multicenter national prospective cohort of patients with OCD of the knee. Inclusion criteria included patients <19 years of age and patients with arthroscopically confirmed mobility status based on the Research on Osteochondritis Dissecans of the Knee arthroscopy classification. Multivariable logistic regression analysis using stepwise model selection was used to determine factors associated with the likelihood of a mobile versus an immobile lesion. A 75% partition of the data was used for model training, and 25% was used as a validation cohort. Quantitative model fit statistics were computed using the holdout data, including sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), along with the corresponding 95% CI. Results: A total of 407 patients in the prospective cohort met inclusion criteria, and 62% were male. The mean ± SD age was 13.7 ± 2.2 years, height 161.8 ± 5.3 cm, and weight 59.2 ± 42.2 kg. Arthroscopic evaluation yielded 235 immobile and 172 mobile lesions. Multivariable analysis determined that the best model to predict lesion mobility included chronologic age ≥14 years (P < .001), effusion on physical examination (P < .001), and any loss of range of motion on physical examination (P = .07), while controlling for male sex (P = .38) and weight >54.4 kg (P = .12). In the 25% holdout validation sample (n = 102), a sensitivity of 83%, a specificity of 82%, and an AUC of 0.89 (95% CI, 0.82-0.95) were achieved with these predictive factors. Conclusion: Age, effusion, and loss of motion can predict knee OCD lesion mobility at the time of arthroscopy. Education about lesion mobility can help with surgical planning and patient and family counseling.

Four New Patient-Reported Outcome Measures Examining Health-Seeking Behavior in Persons With Type 2 Diabetes Mellitus (REDD-CAT): Instrument Development Study.

The management of type 2 diabetes mellitus (T2DM) includes mastery of complex care activities, self-management skills, and routine health care encounters to optimize glucose control and achieve good health. Given the lifelong course of T2DM, patients are faced with navigating complex medical and disease-specific information. This health-seeking behavior is a driver of health disparities and is associated with hospitalization and readmission. Given that health-seeking behavior is a potentially intervenable social determinant of health, a better understanding of how people navigate these complex systems is warranted. To address this need, we aimed to develop new patient-reported outcome (PRO) measures that evaluate health-seeking behavior in persons with T2DM. These new PROs were designed to be included in the Re-Engineered Discharge for Diabetes-Computer Adaptive Test (REDD-CAT) measurement system, which includes several other PROs that capture the importance of social determinants of health. Overall, 225 participants with T2DM completed 56 self-report items that examined health-seeking behaviors. Classical Test Theory and Item Response Theory were used for measurement development. Exploratory factor analysis (EFA; criterion ratio of eigenvalue 1 to eigenvalue 2 being >4; variance for eigenvalue 1 ≥40%) and confirmatory factor analysis (CFA; criterion 1-factor CFA loading <.50; 1-factor CFA residual correlation >.20; comparative fit index ≥0.90; Tucker-Lewis index ≥0.90; root mean square error of approximation <0.15) were used to determine unidimensional sets of items. Items with sparse responses, low-adjusted total score correlations, nonmonotonicity, low factor loading, and high residual correlations of high error modification indices were candidates for exclusion. A constrained graded response model was used to examine item misfit, and differential item functioning was examined to identify item bias. Cronbach α was used to examine internal consistency reliability for the new PROs (criterion ≥0.70), and floor and ceiling effects were examined (criterion ≤20%). Four unidimensional sets of items were supported by EFA (all EFA eigenvalue ratios >4; variance for eigenvalue 1=41.4%-67.3%) and CFA (fit statistics all exceeded criterion values). This included (1) "Health-Seeking Behavior: PCP-Specific" (6 items); (2) "Health-Seeking Behavior: General Beliefs" (13 items); (3) "Health-Seeking Behavior: Family or Friends-Specific" (5 items); and (4) "Health-Seeking Behavior: Internet-Specific" (4 items). All items were devoid of differential item functioning for age, sex, education, or socioeconomic status factors. "Health-Seeking Behavior: General Beliefs" was developed to include both a computer adaptive test and a 6-item short form version; all other PROs were developed as static short forms. The psychometric reliability of these new PROs was supported; internal consistency ranged from acceptable to excellent (Cronbach α=.78-.91), and measures were free of significant floor or ceiling effects (floor effects range: 0%-8.9%; ceiling effects range: 0%-8.4%). The new REDD-CAT Health-Seeking Behavior PROs provide reliable assessments of health-seeking behaviors among those with T2DM.