Using joint probability density to create most informative unidimensional indices: a new method using pain and psychiatric severity as examples.

BACKGROUND: Dimension reduction methods do not always reduce their underlying indicators to a single composite score. Furthermore, such methods are usually based on optimality criteria that require discarding some information. We suggest, under some conditions, to use the joint probability density function (joint pdf or JPD) of p-dimensional random variable (the p indicators), as an index or a composite score. It is proved that this index is more informative than any alternative composite score. In two examples, we compare the JPD index with some alternatives constructed from traditional methods. METHODS: We develop a probabilistic unsupervised dimension reduction method based on the probability density of multivariate data. We show that the conditional distribution of the variables given JPD is uniform, implying that the JPD is the most informative scalar summary under the most common notions of information. B. We show under some widely plausible conditions, JPD can be used as an index. To use JPD as an index, in addition to having a plausible interpretation, all the random variables should have approximately the same direction(unidirectionality) as the density values (codirectionality). We applied these ideas to two data sets: first, on the 7 Brief Pain Inventory Interference scale (BPI-I) items obtained from 8,889 US Veterans with chronic pain and, second, on a novel measure based on administrative data for 912 US Veterans. To estimate the JPD in both examples, among the available JPD estimation methods, we used its conditional specifications, identified a well-fitted parametric model for each factored conditional (regression) specification, and, by maximizing the corresponding likelihoods, estimated their parameters. Due to the non-uniqueness of conditional specification, the average of all estimated conditional specifications was used as the final estimate. Since a prevalent common use of indices is ranking, we used measures of monotone dependence [e.g., Spearman's rank correlation (rho)] to assess the strength of unidirectionality and co-directionality. Finally, we cross-validate the JPD score against variance-covariance-based scores (factor scores in unidimensional models), and the "person's parameter" estimates of (Generalized) Partial Credit and Graded Response IRT models. We used Pearson Divergence as a measure of information and Shannon's entropy to compare uncertainties (informativeness) in these alternative scores. RESULTS: An unsupervised dimension reduction was developed based on the joint probability density (JPD) of the multi-dimensional data. The JPD, under regularity conditions, may be used as an index. For the well-established Brief Pain Interference Inventory (BPI-I (the short form with 7 Items) and for a new mental health severity index (MoPSI) with 6 indicators, we estimated the JPD scoring. We compared, assuming unidimensionality, factor scores, Person's scores of the Partial Credit model, the Generalized Partial Credit model, and the Graded Response model with JPD scoring. As expected, all scores' rankings in both examples were monotonically dependent with various strengths. Shannon entropy was the smallest for JPD scores. Pearson Divergence of the estimated densities of different indices against uniform distribution was maximum for JPD scoring. CONCLUSIONS: An unsupervised probabilistic dimension reduction is possible. When appropriate, the joint probability density function can be used as the most informative index. Model specification and estimation and steps to implement the scoring were demonstrated. As expected, when the required assumption in factor analysis and IRT models are satisfied, JPD scoring agrees with these established scores. However, when these assumptions are violated, JPD scores preserve all the information in the indicators with minimal assumption.

Assessing health-related quality of life using the Wound-QoL-17 and the Wound-QoL-14-Results of the cross-sectional European HAQOL study using item response theory.

For assessing health-related quality of life in patients with chronic wounds, the Wound-QoL questionnaire has been developed. Two different versions exist: the Wound-QoL-17 and the Wound-QoL-14. For international and cross-cultural comparisons, it is necessary to demonstrate psychometric properties in an international study. Therefore, the aim of this study was to test both questionnaires in a European sample, using item response theory (IRT). Participants were recruited in eight European countries. Item characteristic curves (ICC), item information curves (IIC) and differential item functioning (DIF) were calculated. In both questionnaires, ICCs for most items were well-ordered and sufficiently distinct. For items, in which adjacent response categories were not sufficiently distinct, response options were merged. IICs showed that items on sleep and on pain, on worries as well as on day-to-day and leisure activities had considerably high informational value. In the Wound-QoL-14, the item on social activities showed DIFs regarding the country and age. The same applied for the Wound-QoL-17, in which also the item on stairs showed DIFs regarding age. Our study showed comparable results across both versions of the Wound-QoL. We established a new scoring method, which could be applied in international research projects. For clinical practice, the original scoring can be maintained.

Radiation Therapy in Non-Melanoma Skin Cancers: An Italian Survey on Behalf of the Italian Association of Radiotherapy and Clinical Oncology.

AIMS: The National Palliative Care and Interventional Radiotherapy Study Groups of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) carried out a survey whose aim was to obtain a "snapshot" of the real-world practice of nonmelanoma skin cancer (NMSC) treatments in Italy. MATERIALS AND METHODS: The survey was conducted on SurveyMonkey's online interface and was sent via e-mail to our society Radiation Oncologists. RESULTS: Fifty-eight Italian radiation oncologists (ROs), representing 54 centers, answered the survey. Thirteen percent of the ROs declared they treat fewer than 10 NMSC lesions annually, 36% treat between 11 and 20, and 51% treat more than 20 lesions annually. Interventional radiotherapy (IRT) was offered by 25% of the ROs, and every case was reportedly discussed by a multidisciplinary team (71%). Electrons (74%), volumetric modulated arc therapy (V-MAT) (57%), three-dimensional conformal radiotherapy (3D-CRT) (43%), and IRT (26%) were the main treatment options. With external beam radiotherapy (EBRT), 46 and 53 different RT schedules were treated for curative and palliative intent, respectively; whereas for IRT, there were 21 and 7 for curative and palliative intent, respectively. The most popular EBRT curative options were 50-70.95/22-35 fractions (fx) and 50-70 Gy/16-20fx and for EBRT palliative settings, 30Gy/10fx, and 20-35Gy/5fx. For IRT, the most popular curative options were 32-50Gy/8-10fx and 30-54Gy/3-5fx, whereas 30Gy/6fz was the palliative option. Less than 10 re-RT cases were reported in one year in 42.5%, 11-20 cases in 42.5%, and >20 cases annually in 15%. Electrons (61%), VMAT (49%), and BRT (25%) were the most widely used approaches: 20-40Gy in 10fx and 20-25Gy in 5fx were the recommended fractionations. CONCLUSION: The survey shows a variegated reality. A national registry with more detailed data could help in undercover its causes.

Harmonizing the CBCL and SDQ ADHD scores by using linear equating, kernel equating, item response theory and machine learning methods.

Introduction: A problem that applied researchers and practitioners often face is the fact that different institutions within research consortia use different scales to evaluate the same construct which makes comparison of the results and pooling challenging. In order to meaningfully pool and compare the scores, the scales should be harmonized. The aim of this paper is to use different test equating methods to harmonize the ADHD scores from Child Behavior Checklist (CBCL) and Strengths and Difficulties Questionnaire (SDQ) and to see which method leads to the result. Methods: Sample consists of 1551 parent reports of children aged 10-11.5 years from Raine study on both CBCL and SDQ (common persons design). We used linear equating, kernel equating, Item Response Theory (IRT), and the following machine learning methods: regression (linear and ordinal), random forest (regression and classification) and Support Vector Machine (regression and classification). Efficacy of the methods is operationalized in terms of the root-mean-square error (RMSE) of differences between predicted and observed scores in cross-validation. Results and discussion: Results showed that with single group design, it is the best to use the methods that use item level information and that treat the outcome as interval measurement level (regression approach).

Item Parameter Recovery: Sensitivity to Prior Distribution.

Marginal maximum likelihood, a common estimation method for item response theory models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for marginal maximum estimation. In this study, using sample sizes of 1,000 or smaller, not using priors often led to extreme, implausible parameter estimates. Applying prior distributions to the c-parameters alleviated the estimation problems with samples of 500 or more; for the samples of 100, priors on both the a-parameters and c-parameters were needed. Estimates were biased when the mode of the prior did not match the true parameter value, but the degree of the bias did not depend on the strength of the prior unless it was extremely informative. The root mean squared error (RMSE) of the a-parameters and b-parameters did not depend greatly on either the mode or the strength of the prior unless it was extremely informative. The RMSE of the c-parameters, like the bias, depended on the mode of the prior for c.

Uses of infrared thermography in acute illness: a systematic review.

Introduction: Infrared thermography (IRT) is a non-contact, non-ionising imaging modality, providing a visual representation of temperature distribution across a surface. Methods: We conducted a systematic search of indexed and grey literature for studies investigating IRT applications involving patients in acute care settings. Studies were categorised and described along themes identified iteratively using narrative synthesis. Quality appraisal of included studies was performed using the Quality Assessment tool for Diagnostic Accuracy Studies. Results: Of 1,060 unique records, 30 studies were included. These were conducted in emergency departments and intensive care units involving adult, paediatric and neonatal patients. IRT was studied for the diagnosis, monitoring or risk stratification of a wide range of individual conditions. IRT was predominantly used to display thermal change associated with localised inflammation or microcirculatory dysfunction. Existing research is largely at an early developmental stage. Discussion: We recommend that high quality diagnostic validation studies are now required for some clinical applications. IRT has the potential to be a valuable tool in the acute care setting and represents an important area for future research particularly when combined with advances in machine learning technology. Systematic review registration: CRD 42022327619 (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=327619).

Early Detection of Sub-clinical Mastitis in Murrah Buffaloes through Udder Thermogram Analysis during the Natural Progression of Infection.

Mastitis is a global production disease that needs an intelligent solution to tackle effectively. Infrared Thermography (IRT) is a non-invasive technology that could be incorporated into routine day-to-day farm activities to monitor the health status of the animals. In this study, the udder health status was routinely monitored for 30 days among 41 Murrah buffaloes via IRT and the California Mastitis Test (CMT). Further, somatic cell count (SCC), microbial identification, and milk quality parameters were also estimated for representative samples. The thermal imaging data obtained was tabulated and back propagated from the 0th day to the -10th day and front propagated from the 0th day to +10th day for all the udder quarters. Results revealed that on the 0th day, the mean of udder skin surface temperature (USST) and teat skin surface temperature (TSST) showed a difference (p < 0.05) in the sub-clinical mastitis (SCM) and clinical mastitis (CM) affected quarters to the healthy quarters, and their degree of difference was the highest. The indication of infection was signaled during the -9th to -5th day to the 0th day in SCM and CM cases. There was a steep increment in the temperature from -2nd and -1st day to the 0th day of infection. Sometimes, some quarters show an increment in temperature due to mastitis during morning hours but recover by evening milking due to the animal's innate immune system. Thus, the initiation period in which the udder gets assaulted is crucial in the early assessment of SCM by monitoring temperature change using IRT.

The progression of cognitive impairment and its influencing factors in older adults based on longitudinal item response theory.

BACKGROUND: Understanding the development of cognitive impairment and its influencing factors in older adults is crucial for formulating early intervention strategies. PURPOSE: To identify the early dimensions of cognitive impairment and provide a comprehensive description of the trajectories of cognitive decline in older adults prior to death. METHODS: Based on the data of 9883 older adults in the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018, a longitudinal item response theory (Longitudinal IRT) model including covariates was applied to estimate the following parameters. The items in which older adults encountered obstacles first had the least difficulty parameters (δ). The earlier the information curve of an item is lifted, the more information it provides in the early stages of cognitive impairment. Regression coefficient (ß) represents the relative rate of cognitive decline. The cognitive impairment values estimated from the Longitudinal IRT were fitted to a mixed-effects model to examine cognitive impairment trajectories. RESULTS: 'Draw the figure on B Card' (δ = -0.816) was the most challenging item, followed by 'recalling 'clothes" (δ = 0.348) and 'recalling 'apples" (δ = 0.419), while 'name the 'pen" (δ = 4.402) was the simplest instruction for the old adults. The curves of the items in the recall dimension began to rise in the early stages of cognitive impairment. Cognitive impairment of older adults who were women (ß = 0.061), elder (ß = 0.111), smokers (ß = 0.060), living in rural areas (ß = 0.052), not participating in organised social activities (ß = 0.092), suffering from hypertension (ß = 0.022), hyperglycaemia (ß = 0.035), dyslipidaemia (ß = 0.314), low education levels (ß = 0.128), manual labourers (ß = 0.027), and eventual development of dementia (ß = 0.212) exhibited a more accelerated progression. These individuals also had poorer cognitive trajectories. CONCLUSION: Recall is the earliest dimension of cognitive impairment. The subjects who were women, elder, smokers, living in rural areas, not participating in organised social activities, suffering from hypertension, hyperglycaemia, dyslipidaemia, low education, manual labourers, and eventually development of dementia, had a faster descending speed and poorer trajectories.

The Accuracy of Bayesian Model Fit Indices in Selecting Among Multidimensional Item Response Theory Models.

Item response theory (IRT) models are often compared with respect to predictive performance to determine the dimensionality of rating scale data. However, such model comparisons could be biased toward nested-dimensionality IRT models (e.g., the bifactor model) when comparing those models with non-nested-dimensionality IRT models (e.g., a unidimensional or a between-item-dimensionality model). The reason is that, compared with non-nested-dimensionality models, nested-dimensionality models could have a greater propensity to fit data that do not represent a specific dimensional structure. However, it is unclear as to what degree model comparison results are biased toward nested-dimensionality IRT models when the data represent specific dimensional structures and when Bayesian estimation and model comparison indices are used. We conducted a simulation study to add clarity to this issue. We examined the accuracy of four Bayesian predictive performance indices at differentiating among non-nested- and nested-dimensionality IRT models. The deviance information criterion (DIC), a commonly used index to compare Bayesian models, was extremely biased toward nested-dimensionality IRT models, favoring them even when non-nested-dimensionality models were the correct models. The Pareto-smoothed importance sampling approximation of the leave-one-out cross-validation was the least biased, with the Watanabe information criterion and the log-predicted marginal likelihood closely following. The findings demonstrate that nested-dimensionality IRT models are not automatically favored when the data represent specific dimensional structures as long as an appropriate predictive performance index is used.

A Comparison of Response Time Threshold Scoring Procedures in Mitigating Bias From Rapid Guessing Behavior.

Rapid guessing (RG) is a form of non-effortful responding that is characterized by short response latencies. This construct-irrelevant behavior has been shown in previous research to bias inferences concerning measurement properties and scores. To mitigate these deleterious effects, a number of response time threshold scoring procedures have been proposed, which recode RG responses (e.g., treat them as incorrect or missing, or impute probable values) and then estimate parameters for the recoded dataset using a unidimensional or multidimensional IRT model. To date, there have been limited attempts to compare these methods under the possibility that RG may be misclassified in practice. To address this shortcoming, the present simulation study compared item and ability parameter recovery for four scoring procedures by manipulating sample size, the linear relationship between RG propensity and ability, the percentage of RG responses, and the type and rate of RG misclassifications. Results demonstrated two general trends. First, across all conditions, treating RG responses as incorrect produced the largest degree of combined systematic and random error (larger than ignoring RG). Second, the remaining scoring approaches generally provided equal accuracy in parameter recovery when RG was perfectly identified; however, the multidimensional IRT approach was susceptible to increased error as misclassification rates grew. Overall, the findings suggest that recoding RG as missing and employing a unidimensional IRT model is a promising approach.

The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items.

The deployment of statistical models-such as those used in item response theory-necessitates the use of indices that are informative about the degree to which a given model is appropriate for a specific data context. We introduce the InterModel Vigorish (IMV) as an index that can be used to quantify accuracy for models of dichotomous item responses based on the improvement across two sets of predictions (i.e., predictions from two item response models or predictions from a single such model relative to prediction based on the mean). This index has a range of desirable features: It can be used for the comparison of non-nested models and its values are highly portable and generalizable. We use this fact to compare predictive performance across a variety of simulated data contexts and also demonstrate qualitative differences in behavior between the IMV and other common indices (e.g., the AIC and RMSEA). We also illustrate the utility of the IMV in empirical applications with data from 89 dichotomous item response datasets. These empirical applications help illustrate how the IMV can be used in practice and substantiate our claims regarding various aspects of model performance. These findings indicate that the IMV may be a useful indicator in psychometrics, especially as it allows for easy comparison of predictions across a variety of contexts.

Exercise-induced changes in lower limbs skin temperature against plasma ATP among individuals with various type and level of physical activity.

The objective of the study was to examine the lower limbs skin temperature (TSK) changes in response to exhaustive whole-body exercise in trained individuals in reference to changes in plasma adenosine triphosphate (ATP). Eighteen trained participants from distinct sport type ‒ endurance (25.2 ± 4.9 yr) and speed-power (25.8 ± 3.1 yr), and 9 controls (24,9 ± 4,3 yr) ‒ were examined. Lower limbs TSK and plasma ATP measures were applied in parallel in response to incremental treadmill test and during 30-min recovery period. Plasma ATP kinetics were inversely associated to changes in TSK. The first significant decrease in TSK (76-89% of V˙ O2MAX) occurred shortly before a significant plasma ATP increase (86-97% of V˙ O2MAX). During recovery, TSK increased, reaching pre-exercise values (before exercise vs. after 30-min recovery: 31.6 ± 0.4 °C vs. 32.0 ± 0.8 °C, p = 0.855 in endurance; 32.4 ± 0.5 °C vs. 32.9 ± 0.5 °C, p = 0.061 in speed-power; 31.9 ± 0.7 °C vs. 32.4 ± 0.8 °C, p = 0.222 in controls). Plasma ATP concentration did not returned to pre-exercise values in well trained participants (before exercise vs. after 30-min recovery: 699 ± 57 nmol l-1 vs. 854 ± 31 nmol l-1, p < 0.001, η2 = 0.961 and 812 ± 35 nmol l-1 vs. 975 ± 55 nmol l-1, p < 0.001, η2 = 0.974 in endurance and speed-power, respectively), unlike in controls (651 ± 40 nmol l-1 vs. 687 ± 61 nmol·l-1, p = 0.58, η2 = 0.918). The magnitude of TSK and plasma ATP response differed between the groups (p < 0.001, η2 = 0.410 for TSK; p < 0.001, η2 = 0.833 for plasma ATP). We conclude that lower limbs TSK change indirectly corresponds to the reverse course of plasma ATP during incremental exercise and the magnitude of the response depends on the level of physical activity and the associated to it long-term metabolic adaptation.

Psychometric benefits of self-chosen rating scales over given rating scales.

Rating scales are susceptible to response styles that undermine the scale quality. Optimizing a rating scale can tailor it to individuals' cognitive abilities, thereby preventing the occurrence of response styles related to a suboptimal response format. However, the discrimination ability of individuals in a sample may vary, suggesting that different rating scales may be appropriate for different individuals. This study aims to examine (1) whether response styles can be avoided when individuals are allowed to choose a rating scale and (2) whether the psychometric properties of self-chosen rating scales improve compared to given rating scales. To address these objectives, data from the flourishing scale were used as an illustrative example. MTurk workers from Amazon's Mechanical Turk platform (N = 7042) completed an eight-item flourishing scale twice: (1) using a randomly assigned four-, six-, or 11-point rating scale, and (2) using a self-chosen rating scale. Applying the restrictive mixed generalized partial credit model (rmGPCM) allowed examination of category use across the conditions. Correlations with external variables were calculated to assess the effects of the rating scales on criterion validity. The results revealed consistent use of self-chosen rating scales, with approximately equal proportions of the three response styles. Ordinary response behavior was observed in 55-58% of individuals, which was an increase of 12-15% compared to assigned rating scales. The self-chosen rating scales also exhibited superior psychometric properties. The implications of these findings are discussed.

Extending the PROMIS item bank "ability to participate in social roles and activities": a psychometric evaluation using IRT.

OBJECTIVE: Our objective was to explore whether the extension of the PROMIS item bank Ability to Participate in Social Roles and Activities (APSRA) with new items would result in more effective targeting (i.e., selecting items that are appropriate for each individual's trait level), and more reliable measurements across all latent trait levels. METHODS: A sample of 1,022 Dutch adults completed all 35 items of the original item bank plus 17 new items (in Dutch). The new items presented in this publication have been translated provisionally from Dutch into English for presentation purposes. We evaluated the basic IRT assumptions unidimensionality, local independence, and monotonicity. Furthermore, we examined the item parameters, and assessed differential item functioning (DIF) for sex, education, region, age, and ethnicity. In addition, we compared the test information functions, item parameters, and θ scores, for the original and extended item bank in order to assess whether the measurement range had improved. RESULTS: We found that the extended item bank was compatible with the basic IRT assumptions and showed good reliability. Moreover, the extended item bank improved the measurement in the lower trait range, which is important for reliably assessing functioning in clinical populations (i.e., persons reporting lower levels of participation). CONCLUSION: We extended the PROMIS-APSRA item bank and improved its psychometric quality. Our study contributes to PROMIS measurement innovation, which allows for the addition of new items to existing item banks, without changing the interpretation of the scores and while maintaining the comparability of the scores with other PROMIS instruments.

Iron transporter1 OsIRT1 positively regulates saline-alkaline stress tolerance in Oryza sativa.

Soil salinization-alkalization severely affects plant growth and crop yield worldwide, especially in the Songnen Plain of Northeast China. Saline-alkaline stress increases the pH around the plant roots, thereby limiting the absorption and transportation of nutrients and ions, such as iron (Fe). Fe is an essential micronutrient that plays important roles in many metabolic processes during plant growth and development, and it is acquired by the root cells via iron-regulated transporter1 (IRT1). However, the function of Oryza sativa IRT1 (OsIRT1) under soda saline-alkaline stress remains unknown. Therefore, in this study, we generated OsIRT1 mutant lines and OsIRT1-overexpressing lines in the background of the O. sativa Songjing2 cultivar to investigate the roles of OsIRT1 under soda saline-alkaline stress. The OsIRT1-overexpressing lines exhibited higher tolerance to saline-alkaline stress compared to the mutant lines during germination and seedling stages. Moreover, the expression of some saline-alkaline stress-related genes and Fe uptake and transport-related genes were altered. Furthermore, Fe and Zn contents were upregulated in the OsIRT1-overexpressing lines under saline-alkaline stress. Further analysis revealed that Fe and Zn supplementation increased the tolerance of O. sativa seedlings to saline-alkaline stress. Altogether, our results indicate that OsIRT1 plays a significant role in O. sativa by repairing the saline-alkaline stress-induced damage. Our findings provide novel insights into the role of OsIRT1 in O. sativa under soda saline-alkaline stress and suggest that OsIRT1 can serve as a potential target gene for the development of saline-alkaline stress-tolerant O. sativa plants.

Interpretation of course conceptual structure and student self-efficacy: an integrated strategy of knowledge graphs with item response modeling.

BACKGROUND: There is a scarcity of studies that quantitatively assess the difficulty and importance of knowledge points (KPs) depending on students' self-efficacy for learning (SEL). This study aims to validate the practical application of psychological measurement tools in physical therapy education by analyzing student SEL and course conceptual structure. METHODS: From the "Therapeutic Exercise" course curriculum, we extracted 100 KPs and administered a difficulty rating questionnaire to 218 students post-final exam. The pipeline of the non-parametric Item Response Theory (IRT) and parametric IRT modeling was employed to estimate student SEL and describe the hierarchy of KPs in terms of item difficulty. Additionally, Gaussian Graphical Models with Non-Convex Penalties were deployed to create a Knowledge Graph (KG) and identify the main components. A visual analytics approach was then proposed to understand the correlation and difficulty level of KPs. RESULTS: We identified 50 KPs to create the Mokken scale, which exhibited high reliability (Cronbach's alpha = 0.9675) with no gender bias at the overall or at each item level (p > 0.05). The three-parameter logistic model (3PLM) demonstrated good fitness with questionnaire data, whose Root Mean Square Error Approximation was < 0.05. Also, item-model fitness unveiled good fitness, as indicated by each item with non-significant p-values for chi-square tests. The Wright map revealed item difficulty relative to SEL levels. SEL estimated by the 3PLM correlated significantly with the high-ability range of average Grade-Point Average (p < 0.05). The KG backbone structure consisted of 58 KPs, with 29 KPs overlapping with the Mokken scale. Visual analysis of the KG backbone structure revealed that the difficulty level of KPs in the IRT could not replace their position parameters in the KG. CONCLUSION: The IRT and KG methods utilized in this study offer distinct perspectives for visualizing hierarchical relationships and correlations among the KPs. Based on real-world teaching empirical data, this study helps to provide a research foundation for updating course contents and customizing learning objectives. TRIAL REGISTRATION: Not applicable.

Global trends and performances of infrared imaging technology studies on acupuncture: a bibliometric analysis.

Objectives: To summarize development processes and research hotspots of infrared imaging technology research on acupuncture and to provide new insights for researchers in future studies. Methods: Publications regarding infrared imaging technology in acupuncture from 2008 to 2023 were downloaded from the Web of Science Core Collection (WoSCC). VOSviewer 1.6.19, CiteSpace 6.2.R4, Scimago Graphica, and Microsoft Excel software were used for bibliometric analyses. The main analyses include collaboration analyses between countries, institutions, authors, and journals, as well as analyses on keywords and references. Results: A total of 346 publications were retrieved from 2008 to 2023. The quantity of yearly publications increased steadily, with some fluctuations over the past 15 years. "Evidence-Based Complementary and Alternative Medicine" and "American Journal of Chinese Medicine" were the top-cited journals in frequency and centrality. China has the largest number of publications, with the Shanghai University of Traditional Chinese Medicine being the most prolific institution. Among authors, Litscher Gerhard from Austria (currently Swiss University of Traditional Chinese Medicine, Switzerland) in Europe, was the most published and most cited author. The article published by Rojas RF was the most discussed among the cited references. Common keywords included "Acupuncture," "Near infrared spectroscopy," and "Temperature," among others. Explore the relationship between acupoints and temperature through infrared thermography technology (IRT), evaluate pain objectively by functional near-infrared spectroscopy (fNIRS), and explore acupuncture for functional connectivity between brain regions were the hotspots and frontier trends in this field. Conclusion: This study is the first to use bibliometric methods to explore the hotspots and cutting-edge issues in the application of infrared imaging technology in the field of acupuncture. It offers a fresh perspective on infrared imaging technology research on acupuncture and gives scholars useful data to determine the field's hotspots, present state of affairs, and frontier trends.

An Evaluation of Fit Indices Used in Model Selection of Dichotomous Mixture IRT Models.

A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's information criterion (DIC), sample size adjusted BIC (SABIC), relative entropy, the integrated classification likelihood criterion (ICL-BIC), the adjusted Lo-Mendell-Rubin (LMR), and Vuong-Lo-Mendell-Rubin (VLMR). The accuracy of the fit indices was assessed for correct detection of the number of latent classes for different simulation conditions including sample size (2,500 and 5,000), test length (15, 30, and 45), mixture proportions (equal and unequal), number of latent classes (2, 3, and 4), and latent class separation (no-separation and small separation). Simulation study results indicated that as the number of examinees or number of items increased, correct identification rates also increased for most of the indices. Correct identification rates by the different fit indices, however, decreased as the number of estimated latent classes or parameters (i.e., model complexity) increased. Results were good for BIC, CAIC, DIC, SABIC, ICL-BIC, LMR, and VLMR, and the relative entropy index tended to select correct models most of the time. Consistent with previous studies, AIC and AICc showed poor performance. Most of these indices had limited utility for three-class and four-class mixture 3PL model conditions.

Genetic counseling access and service delivery in New York State is variable for parents of infants with complex CFTR genotypes conferring uncertain phenotypes.

BACKGROUND: New York State (NYS) utilizes a three-tiered cystic fibrosis newborn screening (CFNBS) algorithm that includes cystic fibrosis transmembrane conductance regulator (CFTR) gene sequencing. Infants with >1 CFTR variant of potential clinical relevance, including variants of uncertain significance or varying clinical consequence are referred for diagnostic evaluation at NYS cystic fibrosis (CF) Specialty Care Centers (SCCs). AIMS: As part of ongoing quality improvement efforts, demographic, screening, diagnostic, and clinical data were evaluated for 289 CFNBS-positive infants identified in NYS between December 2017 and November 2020 who did not meet diagnostic criteria for CF and were classified as either: CFTR-related metabolic syndrome/CF screen positive, inconclusive diagnosis (CRMS/CFSPID) or CF carriers. RESULTS: Overall, 194/289 (67.1%) had CFTR phasing to confirm whether the infant's CFTR variants were in cis or in trans. Eighteen complex alleles were identified in cis; known haplotypes (p.R117H+5T, p.F508del+p.L467F, and p.R74W+p.D1270N) were the most common identified. Thirty-two infants (16.5%) with all variants in cis were reclassified as CF carriers rather than CRMS/CFSPID. Among 263 infants evaluated at an NYS SCC, 70.3% were reported as having received genetic counseling about their results by any provider, with 96/263 (36.5%) counseled by a certified genetic counselor. CONCLUSION: Given the particularly complex genetic interpretation of results generated by CFNBS algorithms including sequencing analysis, additional efforts are needed to ensure families of infants with a positive CFNBS result have CFTR phasing when needed to distinguish carriers from infants with CRMS/CFSPID, and access to genetic counseling to address implications of CFNBS results.

Evaluating the Douglas-Cohen IRT Goodness of Fit Measure With BIB Sampling of Items.

Methods to detect item response theory (IRT) item-level misfit are typically derived assuming fixed test forms. However, IRT is also employed with more complicated test designs, such as the balanced incomplete block (BIB) design used in large-scale educational assessments. This study investigates two modifications of Douglas and Cohen's 2001 nonparametric method of assessing item misfit, based on A) using block total score and B) pooling booklet level scores for analyzing BIB data. Block-level scores showed extreme inflation of Type I error for short blocks containing 5 or 10 items. The pooled booklet method yielded Type I error rates close to nominal α in most conditions and had power to detect misfitting items. The study also found that the Douglas and Cohen procedure is only slightly affected by the presence of other misfitting items in the block. The pooled booklet method is recommended for practical applications of Douglas and Cohen's method with BIB data.