Consistent Metrics Research Articles

Data Delivery Indicators in EIDA: Designing a Consistent Metrics System in a Distributed Services Environment

The European Integrated Data Archive (EIDA) is a data service within Observatories and Research Facilities for European Seismology (ORFEUS) which distributes seismological waveform data across, presently, 12 federated data centres, so-called EIDA nodes. EIDA is also integrated into the European Research Infrastructure EPOS (European Plate Observing System). The increasing need of quantifying the scientific impact of such Research Infrastructures at all levels of operations has led EIDA to develop a unified system able to provide global indicators for data delivery across all EIDA nodes. To achieve this goal, EIDA implemented a system where the numbers are collected and aggregated by each EIDA node and ingested in a centralised database, accessible through an online API. The successful implementation of the system stems from carefully identifying the relevant indicators for well-defined groups of users. The implementation combines simplicity and robustness whilst respecting the distributed design of EIDA. The architecture and implementation choices may be of interest to other multi-site data distribution research infrastructures that face the challenge of providing meaningful key performance indicators.

Predictive Regression Modeling for Forecasting Graduation Duration in Online Offsite Degree Program

The demand for Information Technology (IT) professionals continues to rise across various sectors, where theyplay vital roles. However, the supply of IT graduates often fails to meet industry needs and this is a huge problem for the SriLankan IT Industry (National IT-BPM Workforce Survey – 2019). In this context, this study presents a predictive regressionmodelling approach to predict graduation duration in the Bachelor of Information Technology (BIT) degree program at theUniversity of Moratuwa, Sri Lanka. It integrates demographic data—student district, birth year, AL results, OL maths grade,gender, employability status, occupation, and AL stream—along with academic performance indicators like diplomacompletions and higher diploma completions. After evaluating the suggested features, the key findings indicate thesignificance of certain features, notably the number of semesters taken to complete the diploma, higher diploma, and thedegree. Additionally, demographic factors such as district, birth year, AL results, OL maths grade, gender, and employabilitystatus were found to be important. The regression analysis was carried out using the Orange data mining tool (Orange DataMining). Various algorithms, including random forest, neural network, linear regression, and k-nearest neighbours (kNN),were used to develop predictive models. By adjusting parameters such as metrics, weights, number of neighbours, numberof iterations, and training dataset size, the models were optimised to better fit the dataset. Training and testing the modelsrevealed consistent error metrics, including MSE, RMSE, MAE, and R^2, validating the accuracy of predictions. Byconsidering the least and reasonable error in each model, the most suitable model to fit the given dataset was selected.The prediction model accurately forecasted graduation duration for subsequent academic batches, demonstrating itseffectiveness in predicting student progress in the program. This research contributes to understanding the factorsinfluencing graduation duration in a distance learning context and provides insights for educational institutions to optimiseprogram planning and student support initiatives. Additionally, it is a good indicator to the companies to gain a betterunderstanding of the availability of future workforce.

Understanding Test Convention Consistency as a Dimension of Test Quality

Unit tests must be readable to help developers understand and evolve production code. Most existing test quality metrics assess test code's ability to detect bugs. Few metrics focus on test code's readability. One standard approach to improve readability is the consistent application of conventions. We investigated test convention consistency as a dimension of test quality. We formalized test suite consistency as the extent to which alternatives are used within a code base and introduce two complementary metrics to capture this extent. We elaborated a catalog of over 30 test conventions for the Java language organized in 10 convention classes that group mutual alternatives. We developed tool support to detect occurrences of conventions, compute consistency metrics over a test suite, and view occurrences of conventions in the corresponding code. We applied our tools to study the consistency of the test suites of 20 large open-source Java projects. The study validates the design of the test convention classes, provides descriptive statistics on the range of consistency values for ten different convention classes, and enables us to link observed changes in consistency values to specific events in the change history of our target systems, thus providing evidence of the construct validity of the metrics. We conclude that analyzing test suite consistency via static analysis shows promise as a practical approach to help improve test suite quality.

B-142 Using Big Data Analytics to Measure the Multi-year Consistency of Sigma metrics for Vitros Cardiac Assays

Abstract Background Sigma metrics assess lab quality by incorporating precision, accuracy and total error allowed (TEa). Sigma metrics report quality on a 0-6 scale, with 6 representing World-Class quality. Due to logistical constraints, Sigma metrics are often calculated with limitations such as a small number of analyzers, single reagent lot or short duration. QuidelOrtho developed a novel approach using big data analytics for the objective, automated calculation of Sigma metrics across thousands of Vitros(R) analyzers running in real lab conditions. This approach was previously used to report the Sigma metric performance for 115 assays using data from 2022. Of particular interest is the consistency of quality levels for Vitros cardiac assays assessed in the study. Methods The methods follow the previous approach using the same Python algorithm to extract QC data from Jan. to Nov. 2023 (inclusive). A QC fluid lot from the study period was selected for each analyte to align with the fluid level reported in the 2022 study, which was based on medical relevance. Metrics were calculated for each set of QC data that originated from a single analyzer, reagent lot and calibration curve; the median Sigma metric is reported in the Results. Proficiency testing limits were utilized as TEa limits where available, with preference to CLIA 2024 limits. Results The following assays reported Sigma metrics as >6 using both 2022 and 2023 data: apolipoprotein A1, apolipoprotein B, CK-MB, dLDL, LDH, myoglobin, NT-proBNP, and triglycerides. Assays with higher metrics using 2023 data: CK (5.2 vs 5.5), dHDL (5.8 vs >6), hsTnI (4.1 vs 4.3), and NT-proBNP2 (4.2 vs >6). Assays with lower metrics using 2023 data: cholesterol (5.8 vs. 5.3) and homocysteine (>6 vs 5.9). CKMB2 (>6) is a newer assay that did not have metrics reported in 2022. Vitros cardiac assays report metrics within 0.5 Sigma of the 2022 metrics. An exception is NT-proBNP2, which changed from 4.2 to >6. However, the 2023 analysis has almost double the number of Vitros analyzers (51 vs 90), representing new use of NT-proBNP2. Conclusions Vitros cardiac assays report consistent metrics when using 2023 data compared to 2022. All Vitros cardiac assays assessed report Sigma metrics of 4 or greater, indicating they achieve results within the corresponding allowable error.

A framework for medical physics compensation in an academic department.

Compensation is a key component of career satisfaction and professional growth. A new compensation model was developed to provide a framework for career growth and a compensation ladder for medical physicists with clinical responsibilities in an academic radiation oncology department. The goals for the new model were: (1) create a market competitive plan to support recruitment and retention of top physics talent, (2) incentivize clinical effort, innovation, citizenship/professional service, and academic achievement, (3) provide compensation growth opportunities separate from medical school promotions, and (4) create consistent, transparent, and fair metrics applicable to all clinical physicists in the department. The model includes a base salary, and credits for board certification, clinical tier, leadership, and academic level. Further, metrics were developed to inform the clinical tier. Years of experience is not explicitly included in the model. The model was successfully implemented for clinical physicists in a relatively large academic radiation oncology department.

Supervised star, galaxy, and QSO classification with sharpened dimensionality reduction

We explored the use of broadband colors to classify stars, galaxies, and quasi-stellar objects (QSOs). Specifically, we applied sharpened dimensionality reduction (SDR)-aided classification to this problem, with the aim of enhancing cluster separation in the projections of high-dimensional data clusters to allow for better classification performance and more informative projections. The main objective of this work was to apply SDR to large sets of broadband colors derived from the CPz catalog to obtain projections with clusters of star, galaxy, and QSO data that exhibit a high degree of separation. The SDR method achieves this by combining density-based clustering with conventional dimensionality-reduction techniques. To make SDR scalable and have the ability to project samples using the earlier-computed projection, we used a deep neural network trained to reproduce the SDR projections. Subsequently classification was done by applying a $k$-nearest neighbors ($k$-NN) classifier to the sharpened projections. Based on a qualitative and quantitative analysis of the embeddings produced by SDR, we find that SDR consistently produces accurate projections with a high degree of cluster separation. A number of projection performance metrics are used to evaluate this separation, including the trustworthiness, continuity, Shepard goodness, and distribution consistency metrics. Using the $k$-NN classifier and consolidating the results of various data sets, we obtain precisions of 99.7<!PCT!>, 98.9<!PCT!>, and 98.5<!PCT!> for classifying stars, galaxies, and QSOs, respectively. Furthermore, we achieve completenesses of 97.8<!PCT!>, 99.3<!PCT!>, and 86.8<!PCT!>, respectively. In addition to classification, we explore the structure of the embeddings produced by SDR by cross-matching with data from Gaia DR3, Galaxy Zoo 1, and a catalog of specific star formation rates, stellar masses, and dust luminosities. We discover that the embeddings reveal astrophysical information, which allows one to understand the structure of the high-dimensional broadband color data in greater detail. We find that SDR-aided star, galaxy, and QSO classification performs comparably to another unsupervised learning method using hierarchical density-based spatial clustering of applications with noise (HDBSCAN) but offers advantages in terms of scalability and interpretability. Furthermore, it outperforms traditional color selection methods in terms of QSO classification performance. Overall, we demonstrate the potential of SDR-aided classification to provide an accurate and physically insightful classification of astronomical objects based on their broadband colors.

On-Substrate Preparation of a Poly(triphenylamino azomethine) for Electrochromic Devices.

An electroactive polyazomethine was prepared directly on a transparent electrode by the polycondensation of bis(triphenylamine) dialdehyde and its complementary methoxytriphenylamine diamine. The spray-and-bake method of coating the electrode for preparing electrochromic layers could be upscaled to prepare working devices larger than standard test devices. The film prepared by thermally annealing the complementary monomers was both electroactive and switched its color with an applied potential. The yellow electrochromic polyazomethine could be electrochemically oxidized reversibly to obtain a blue film. The electrochromic test device fabricated from the polyazomethine was operated upwards of 1 h for performance assessment. The electrochromic response times of the electrochromic device were ca. 3.3 and 1.2 s for the coloration and bleaching, respectively. The upscaled device prepared by the straightforward coating approach had consistent metrics with the small-area test device.

Validation of the dispositional adult hyperfocus questionnaire (AHQ-D)

Hyperfocus (HF), or intense, deep concentration on a task, has gained significant research attention in recent years, particularly in regard to clinical populations such as Attention-Deficit/Hyperactivity Disorder (ADHD). The present work aims to provide validation of the 12-item dispositional adult hyperfocus questionnaire (AHQ-D) as a quantitative metric of HF in adults. We preregistered the study design and hypotheses. We administered the AHQ-D and several additional questionnaires to 347 adults (mean ± SD age: 33 ± 11 years; 47% female). Exploratory factor analysis revealed high factor loadings (0.57–0.81) on a single HF factor; item response theory analysis suggested that the questionnaire items had high discrimination and covered a wide range of responses; and we report strong internal consistency metrics (Cronbach’s alpha 0.93, mean split-half reliability 0.93). Replicating our previous work, HF was positively correlated with Conners’ Adult ADHD Rating Scale (CAARS) scores (r(345) = 0.53), suggesting that HF may be related to ADHD traits (though in this sample we did not specifically recruit individuals with ADHD). The AHQ-D demonstrated the hypothesized convergent validity; HF on the AHQ-D was positively correlated with HF measured using a different HF scale (r(344) = 0.69), as well as flow (r(345) = 0.12) and mind wandering (r(345) = 0.39) scores. AHQ-D HF scores showed a weak negative correlation with grit (r(345) = − 0.29). Though there was a weak negative correlation between HF and social desirability response tendency (r(345) = − 0.24), suggesting that those who care more about what others think may report less HF, there was no relationship between HF and extrasensory perception beliefs (r(345) = 0.01), suggesting that participants were not simply biased in their response tendencies. Taken together, we demonstrate strong scale metrics for the AHQ-D, the expected convergent validity, and a general lack of response bias, in addition to replicating our previous association of HF with ADHD traits. We suggest that the AHQ-D can be confidently used in future work as a valid way to measure HF in adults.

Employing Siamese Networks as Quantitative Biomarker for Assessing the Effect of Dolphin-Assisted Therapy on Pediatric Cerebral Palsy.

This study explores the potential of using a Siamese Network as a biomarker for assessing the effectiveness of Dolphin-Assisted Therapy (DAT) in children with Spastic Cerebral Palsy (SCP). The problem statement revolves around the need for objective measures to evaluate the impact of DAT on patients with SCP, considering the subjective nature of traditional assessment methods. The methodology involves training a Siamese network, a type of neural network designed to compare similarities between inputs, using data collected from SCP patients undergoing DAT sessions. The study employed Event-Related Potential (ERP) and Fast Fourier Transform (FFT) analyses to examine cerebral activity and brain rhythms, proposing the use of SNN to compare electroencephalographic (EEG) signals of children with cerebral palsy before and after Dolphin-Assisted Therapy. Testing on samples from four children yielded a high average similarity index of 0.9150, indicating consistent similarity metrics before and after therapy. The network is trained to learn patterns and similarities between pre- and post-therapy evaluations, in order to identify biomarkers indicative of therapy effectiveness. Notably, the Siamese Network's architecture ensures that comparisons are made within the same feature space, allowing for more accurate assessments. The results of the study demonstrate promising findings, indicating different patterns in the output of the Siamese Network that correlate with improvements in symptoms of SCP post-DAT. Confirming these observations will require large, longitudinal studies but such findings would suggest that the Siamese Network could have utility as a biomarker in monitoring treatment responses for children with SCP who undergo DAT and offer them more objective as well as quantifiable manners of assessing therapeutic interventions. Great discrepancies in neuronal voltage perturbations, 7.9825 dB on average at the specific samples compared to the whole dataset (6.2838 dB), imply a noted deviation from resting activity. These findings indicate that Dolphin-Assisted Therapy activates particular brain regions specifically during the intervention.

Understanding the global subnational migration patterns driven by hydrological intrusion exposure

Amid the escalating global climatic challenges, hydrological risks significantly influence human settlement patterns, underscoring the imperative for an in-depth comprehension of hydrological change’s ramifications on human migration. However, predominant research has been circumscribed to the national level. The study delves into the nonlinear effects of hydrological risks on migration dynamics in 46,776 global subnational units. Meanwhile, leveraging remote sensing, we procured globally consistent metrics of hydrological intrusion exposure, offering a holistic risk assessment encompassing hazard, exposure, and vulnerability dimensions, thus complementing previous work. Here, we show that exposure is the primary migration driver, surpassing socioeconomic factors. Surrounding disparities further intensified exposure’s impact. Vulnerable groups, especially the economically disadvantaged and elderly, tend to remain in high-risk areas, with the former predominantly migrating within proximate vicinities. The nonlinear analysis delineates an S-shaped trajectory for hydrological exposure, transitioning from resistance to migration and culminating in entrapment, revealing dependence on settlement resilience and adaptability.

An evaluation tool for assessing coral restoration efforts

The ever-increasing need for coral restoration as a tool available to mitigate reef declines and aid in the recovery of lost ecosystem services requires improving restoration performance over time through an adaptive management framework to evaluate the status of restoration programs using uniform, consistent metrics. An evaluation tool, presented herein, allows restoration practitioners and managers to self-evaluate the robustness of each project and identify successful metrics, those metrics that need special attention, and changes to restoration strategies that can improve performance and aid recovery. This tool is designed to allow programs to track the progress of each key metric over time to assist in improving upon successes and learning from failures. The metrics within this restoration evaluation tool focus on published best-management practices and have resulted from extensive research conducted by restoration experts over the past 20 years. Common metrics of growth and survival are included, in addition to parameters vital to the operational success of restoration programs, such as coral reproduction, recruitment of associated reef taxa, increasing habitat for reef fisheries, and improving overall reef habitat. Five Caribbean restoration programs, each with at least 15 years of restoration experience, are presented as case studies. Each program was evaluated based on six restoration categories including: field-based nurseries, outplantings, programmatic management, education and outreach, event-driven restoration, and socioeconomic restoration. Category-specific metrics were scored with a binary scoring system and summarized using a stop-light indicator framework, where the resulting color/score indicates the operations tatus of the different program components (Scores >75% = green/successful; 50–74.9% = light green > yellow > orange/intermediate; <49.9% = red/sub-optimal). Composite scores may be used to evaluate individual projects, overall restoration programs, or even large-scale state of regional restoration plans. Overall, four of the five programs scored >75% indicating most of these programs are performing well, are versatile, well managed, and sustainable. Outside of environmental factors and large-scale disturbance events, many programs described resource limitations, including funding and staffing, as reasons for scoring low on some metrics. A holistic evaluation rubric incorporated into programmatic self-assessment can ensure that restoration projects and programs are working towards success and sustainability.

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia.

The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.

Factual consistency evaluation of summarization in the Era of large language models

Factual inconsistency with source documents in automatically generated summaries can lead to misinformation or pose risks. Existing factual consistency (FC) metrics are constrained by their performance, efficiency, and explainability. Recent advances in Large language models (LLMs) have demonstrated remarkable potential in text evaluation but their effectiveness in assessing FC in summarization remains underexplored. Prior research has mostly focused on proprietary LLMs, leaving essential factors that affect their assessment capabilities unexplored. Additionally, current FC evaluation benchmarks are restricted to news articles, casting doubt on the generality of the FC methods tested on them. In this paper, we first address the gap by introducing TreatFact—a dataset of LLM-generated summaries of clinical texts, annotated for FC by domain experts. Moreover, we benchmark 11 LLMs for FC evaluation across news and clinical domains and analyse the impact of model size, prompts, pre-training and fine-tuning data. Our findings reveal that despite proprietary models prevailing on the task, open-source LLMs lag behind. Nevertheless, there is potential for enhancing the performance of open-source LLMs through increasing model size, expanding pre-training data, and developing well-curated fine-tuning data. Experiments on TreatFact suggest that both previous methods and LLM-based evaluators are unable to capture factual inconsistencies in clinical summaries, posing a new challenge for FC evaluation.

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.

Perianal Fistulizing Crohn's Disease: Outcomes of Surgical Repairs and Current State of Stem Cell-Based Therapies

AbstractPerianal fistulizing Crohn's disease is one of the most disabling phenotypes of Crohn's disease, due to the severe impairment in quality of life including social and personal wellbeing. A multimodal approach with patient-tailored care is the key to optimal management of this condition. Medical therapy is needed to optimize the luminal disease, and surgical intervention is required to control any associated perianal sepsis and attempt palliative or definitive fistula repair. While several medical and surgical options are available, the majority of patients continue to have symptomatic disease. Fortunately, this continues to drive novel innovations which are revolutionizing the treatment and outcomes of perianal fistulizing Crohn's disease. However, there continues to be a need for randomized trials and consistent metrics utilized for classification and treatment outcomes in order to accurately describe optimal treatment outcomes.

Evaluating implementation of a community-focused patient navigation intervention at an NCI-designated cancer center using RE-AIM

BackgroundPatient navigation is an evidence-based intervention that reduces cancer health disparities by directly addressing the barriers to care for underserved patients with cancer. Variability in design and integration of patient navigation programs within cancer care settings has limited this intervention’s utility. The implementation science evaluation framework, RE-AIM, allows quantitative and qualitative examination of effective implementation of patient navigation programs into cancer care settings.MethodsThe Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to evaluate implementation of a community-focused patient navigation intervention at an NCI-designated cancer center between June 2018 and October 2021. Using a 3-month longitudinal, non-comparative measurement period, univariate and bivariate analyses were conducted to examine associations between participant-level demographics and primary (i.e., barrier reduction) and secondary (i.e., patient-reported outcomes) effectiveness outcomes. Mixed methods analyses were used to examine adoption and delivery of the intervention into the cancer center setting. Process-level analyses were used to evaluate maintenance of the intervention.ResultsParticipants (n = 311) represented a largely underserved population, as defined by the National Cancer Institute, with the majority identifying as Hispanic/Latino, having a household income of $35,000 or less, and being enrolled in Medicaid. Participants were diagnosed with a variety of cancer types and most had advanced staged cancers. Pre-post-intervention analyses indicated significant reduction from pre-intervention assessments in the average number of reported barriers, F(1, 207) = 117.62, p < .001, as well as significant increases in patient-reported physical health, t(205) = − 6.004, p < .001, mental health, t(205) = − 3.810, p < .001, self-efficacy, t(205) = − 5.321, p < .001, and satisfaction with medical team communication, t(206) = − 2.03, p = .029. Referral patterns and qualitative data supported increased adoption and integration of the intervention into the target setting, and consistent intervention delivery metrics suggested high fidelity to intervention delivery over time. Process-level data outlined a successful transition from a grant-funded community-focused patient navigation intervention to an institution-funded program.ConclusionsThis study utilized the implementation science evaluation framework, RE-AIM, to evaluate implementation of a community-focused patient navigation program. Our analyses indicate successful implementation within a cancer care setting and provide a potential guide for other oncology settings who may be interested in implementing community-focused patient navigation programs.

Piloting Rasch model scoring of the National Eye Institute Visual Function Questionnaire in uveitis

IntroductionThe National Eye Institute Visual Function Questionnaire (NEI VFQ) is a common patient-reported outcome measure (PROM) in uveitis trials. Its psychometric properties using state-of-the-art scoring based on Rasch models, a latent trait model that improves accuracy of PROMs assessment, has not yet been investigated.MethodsThe study participants were recruited online from uveitis patient organizations, where individuals self-reported their uveitis diagnosis and visual acuity level. These participants then completed the NEI VFQ-25. The visual function (VF) and socioemotional (SE) subscales were psychometrically analysed in terms of item fit, targeting, internal consistency, dimensionality, and differential item functioning (DIF), using Rasch models. Criterion validity was examined based on associations between NEI VFQ person measures and recent visual acuity (VA) levels.ResultsNinety-nine participants recruited online from uveitis patient organizations (68 women, 31 men; mean age 50 ± 15 years; 46.5% self-reported receiving systematic therapy for uveitis, 0.6% NEI VFQ-25 missing data) were included. The mean difficulty of items was lower than the average person ability. None of the items demonstrated misfit to an extent that would induce noise into the measurement. The consistency metrics person reliability and person separation index of the subscales were 0.85 and 2.34 (NEI VFQ-VF), 0.86 and 2.52 (NEI VFQ-SE), respectively. There was no evidence of multidimensionality and none of the items showed DIF by gender. The differences between item and person measures were 1.44 (NEI VFQ-VF) and 1.03 (NEI VFQ-SE). NEI VFQ-25 person measures were significantly lower in participants with visual impairment (all p values ≤ 0.007).ConclusionRasch model-based scoring of the re-engineered NEI VFQ-25 demonstrates acceptable internal consistency, item fit and construct validity for assessing two key domains of quality of life in individuals self-reporting uveitis. The PROM was targeted at a higher level of difficulty than present in our heterogeneous sample.

Impact of PET Reconstruction on Amyloid-β Quantitation in Cross-Sectional and Longitudinal Analyses.

Amyloid-β (Aβ) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aβ PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aβ-negative [Aβ-], 45 Aβ-positive [Aβ+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aβ- or Aβ+ visually. For each reconstruction, Aβ CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aβ- and Aβ+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aβ-, 33 Aβ+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aβ- or Aβ+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aβ CLs were calculated using CapAIBL. Since linear Aβ accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aβ accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aβ accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aβ- and the Aβ+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aβ- CLs increased for a FWHM of 4.5 mm or more, whereas Aβ+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aβ- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aβ accumulators in the Aβ- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aβ PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aβ- and Aβ+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aβ accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.

Yoga-Based Interventions in Low-Income Populations: A Scoping Review.

Introduction: The popularity of yoga has surged in recent years; however, yoga practitioners have remained a largely homogenous population. Research reflects that most practitioners are of a higher socioeconomic status. There are access barriers to yoga for lower income individuals, likely due to factors such as financial constraints and logistical challenges. The primary goal of this review was to synthesize literature on yoga research among low-income populations and better understand the feasibility and acceptability of such interventions. A secondary goal was to assess the consistency of metrics for reporting feasibility and acceptability across such studies using the CheckList Standardizing the Reporting of Interventions for Yoga (CLARIFY) guidelines as a framework. Third, the authors sought to propose additional standardized CLARIFY guidelines that may enhance reporting on the diversity of yoga research populations, adherence, and retention. Methods: The electronic databases PubMed, PsycINFO, Cochrane Central Register of Controlled Trials, and Google Scholar were searched in May 2022 using a prespecified search string. Articles assessing a yoga intervention in predominantly low-income adult populations were deemed eligible for inclusion. Results: The search resulted in 512 potential articles. Eleven were deemed eligible for inclusion. The included studies reported mostly positive effects of yoga on the target outcome (i.e., pain/disability, quality of life/wellness, and psychiatric symptoms). Recruitment and retention data showed generally good attendance and high study completion rates. Common study design components included recruitment embedded within preexisting medical settings, proximal yoga locations, and mitigation of yoga-related costs. Finally, the authors noted inconsistency in the reporting of adherence, retention, and other sociodemographic characteristics of participants and yoga instructors (e.g., race, ethnicity, and income). Discussion: Yoga may promote physical and mental health for low-income individuals. Important facilitators to access are noted, such as proximal study settings, as well as barriers such as the need for childcare that can be addressed in future research. In addition, several study design considerations could help address the specific needs of low-income participants in yoga research, such as compensating participants, recruiting within existing medical settings, and providing yoga-related equipment at no cost. Finally, the authors suggest specific ways to enhance reporting of study metrics related to socioeconomic diversity, by adding to the preexisting CLARIFY guidelines.

Rethinking Two-Stage Referring Expression Comprehension: A Novel Grounding and Segmentation Method Modulated by Point

As a fundamental and challenging task in the vision and language domain, Referring Expression Comprehension (REC) has shown impressive improvements recently. However, for a complex task that couples the comprehension of abstract concepts and the localization of concrete instances, one-stage approaches are bottlenecked by computing and data resources. To obtain a low-cost solution, the prevailing two-stage approaches decouple REC into localization (region proposal) and comprehension (region-expression matching) at region-level, but the solution based on isolated regions cannot sufficiently utilize the context and is usually limited by the quality of proposals. Therefore, it is necessary to rebuild an efficient two-stage solution system. In this paper, we propose a point-based two-stage framework for REC, in which the two stages are redefined as point-based cross-modal comprehension and point-based instance localization. Specifically, we reconstruct the raw bounding box and segmentation mask into center and mass scores as soft ground-truth for measuring point-level cross-modal correlations. With the soft ground-truth, REC can be approximated as a binary classification problem, which fundamentally avoids the impact of isolated regions on the optimization process. Remarkably, the consistent metrics between center and mass scores allow our system to directly optimize grounding and segmentation by utilizing the same architecture. Experiments on multiple benchmarks show the feasibility and potential of our point-based paradigm. Our code available at https://github.com/VILAN-Lab/PBREC-MT.