Predictive Validity Research Articles

Purpose In recent years, supply chain risk management capabilities (SCRMC) has become integral to enhancing resilience, mitigating disruptions and ensuring consistent operations in supply chains. SCRMC has gained significant attention from researchers, leading to different fragmented conceptualizations. This study aims to reconcile this fragmentation by proposing a theory-driven reconceptualization of the construct and a multidimensional scale that captures SCRMC’s complex nature. Design/methodology/approach A two-step mixed methodology was adopted. First, drawing on existing supply chain risk management literature and expert discussions, scale items were generated and refined using the Q-sort method. Second, the scale was validated using survey data from 301 supply chain professionals across various industries in the USA. Findings The findings reveal three distinct yet interrelated dimensions – warning capability, robustness capability and resilience capability – which exhibit strong psychometric properties. The scale also demonstrates high levels of validity and reliability and improves predictive validity compared to previously developed scales. Originality/value This study offers a comprehensive and holistic understanding of SCRMC and serves as a reliable tool for measuring SCRMC, providing a foundation and analytical consistency for both practical application and future research.

Background: Polygenic risk scores (PRS) offer promising avenues for stratifying myocardial infarction (MI) risk and informing precision prevention. However, most PRS are derived from European-ancestry datasets, raising concerns about predictive validity and clinical equity across ancestrally diverse populations. Goals/Aims: To evaluate the predictive performance, calibration, and clinical utility of MI-related PRS across global ancestries, and to identify strategies that enhance transethnic applicability. Methods: We conducted a systematic review and meta-analysis in accordance with PRISMA guidelines. PubMed, Embase and Scopus were searched through March 2025 for studies reporting ancestry-stratified PRS performance for MI. Primary outcomes included area under the curve (AUC), odds ratio (OR) per standard deviation of PRS, observed-to-expected event ratios (O/E), and reclassification metrics (net reclassification improvement [NRI], integrated discrimination improvement [IDI]). Random-effects models were used for pooled estimates, with subgroup analyses by ancestry (European, African, South Asian, East Asian, Hispanic/Latino, admixed) and PRS construction method. Meta-regression, heterogeneity (I2), and risk-of-bias assessments were applied. Publication bias was evaluated via funnel plots and Egger’s test. Results: Forty-six studies encompassing 1.32 million individuals across six ancestral groups were included. In European cohorts, pooled PRS AUC was 0.74 (95% CI: 0.72–0.76), compared to 0.63 (95% CI: 0.60–0.66) in African and 0.66 (95% CI: 0.64–0.68) in South Asian populations (p < 0.001 for heterogeneity). Calibration was poorer in non-European groups (O/E >1.4), indicating systemic overestimation of risk. While PRS improved net reclassification in European cohorts (NRI: +12.1%), clinical utility was limited in African ancestry (NRI: +2.3%). Meta-regression revealed that ancestry-specific allele frequency adjustment and inclusion of multi-ancestry training datasets significantly improved PRS performance (p < 0.01). Conclusion: Current PRS for MI demonstrate reduced accuracy and suboptimal calibration in non-European populations, undermining clinical utility and exacerbating genomic health disparities. These findings highlight the urgent need for globally inclusive genomic data and ancestry-aware PRS optimization. Implementation of strategies is critical for equitable risk prediction tools and for aligning precision cardiology with global clinical practice.

Responsible drinking is a common term used by a variety of stakeholders. Although many people and organizations discuss responsible drinking, its meaning remains unclear. Researchers have begun to scrutinize the concept, critically questioning its utility, definition, and distinction from other alcohol-related constructs; however, its measurement has remained limited. Accordingly, we present a series of studies describing the development of the Responsible Drinking Inventory (RDI), a new 18-item self-administered measure of responsible drinking beliefs and behaviors. We report upon the creation and the psychometric properties of the RDI across six primary studies. Examinations of the RDI indicated appropriate reliability and validity, including convergent and divergent validity, as well as known groups and predictive validity. The RDI appears to provide information that is consistent with alcohol safety-oriented measures, such as the Protective Behavioral Strategies Scale, and distinct from alcohol harm measures, such as the Alcohol Dependence Scale. The RDI predicts acute consequences of drinking behavior 3 months in the future. This new measure provides unique insights into the nature of responsible drinking and a concise, yet comprehensive way to assess this concept. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The genetic architecture of essential tremor (ET) remains incompletely understood. A key challenge is translating genome-wide association study (GWAS) loci into specific effector genes to elucidate disease mechanisms and develop targeted therapies. To implement a multistage computational framework to prioritize high-confidence candidate genes for ET and to assess these predictions against publicly available, patient-derived transcriptomic data. We employed a convergent evidence strategy to prioritize genes, integrating cross-tissue (UTMOST) and tissue-specific (FUSION) transcriptome-wide association studies (TWAS) with gene-based association tests (MAGMA). Prioritized genes were subjected to causal inference analyses (summary-data-based Mendelian randomization [SMR] and colocalization), co-expression network analysis (GeneMANIA), and pharmacogenomic analysis (DGIdb). We leveraged spatial transcriptomics to characterize gene expression patterns across cortical layers and cell types. Finally, we validated computational predictions using two independent post-mortem brain datasets from ET patients and controls. Our prioritization pipeline identified 12 high-confidence candidate genes. Co-expression network analysis revealed 83.3% of candidates exhibit functional relationships, forming three modules centered on RNA processing (NRBP1), metabolic regulation (SLC5A6), and nucleotide synthesis (CAD). Pharmacogenomic analysis demonstrated 66.7% of candidates possess therapeutic target potential. Spatial transcriptomics revealed preferential expression in cortical Layer 5 pyramidal neurons. However, validation in post-mortem cerebellar tissue showed no significant differential expression. Our study provides a robust pipeline for ET gene prioritization and puts forward a novel cortical hypothesis for the disease. The discordance between strong computational predictions and their lack of validation in available patient tissue highlights a critical gap in the field. © 2025 International Parkinson and Movement Disorder Society.

This study aimed to develop and validate a prognostic model for hepatocellular carcinoma (HCC) patients undergoing liver resection, using the functional liver imaging score (FLIS) derived from hepatobiliary-specific contrast-enhanced magnetic resonance imaging (MRI). A total of 694 pathologically confirmed HCC patients who underwent hepatobiliary-specific MRI with either gadoxetic acid or gadobenate dimeglumine and subsequent liver resection were included. FLIS was calculated by assigning 0-2 points to three hepatobiliary-phase MRI features: hepatic enhancement, biliary excretion, and portal vein signal intensity. Multivariable Cox regression identified AFP level, tumor size, and extent of resection as independent predictors of overall survival (OS). FLIS ≤ 2, alpha-fetoprotein (AFP) > 400ng/mL, tumor size > 5cm, and major resection were identified as independent predictors of worse OS. A predictive model combining these factors demonstrated excellent prognostic performance, with Harrell's concordance indices of 0.91 in the training cohort and 0.96 internal validation cohort, and 0.94 in external validation cohort. The FLIS-based model significantly outperformed FLIS alone and conventional clinical models (p < 0.05). Kaplan-Meier survival analysis showed that low-risk patients had significantly better OS and recurrence-free survival (RFS) compared to high-risk patients across all cohorts (p < 0.05). FLIS is a simple, non-invasive imaging biomarker for evaluating liver function and predicting outcomes in HCC patients. When integrated with key clinical variables, the FLIS-based model demonstrates excellent discrimination and calibration for OS and RFS, providing accurate postoperative prognostic stratification and showing great potential for guiding surveillance and improving long-term survival outcomes in future clinical applications.

Validity and reliability of the pediatric pressure ulcer prediction and evaluation tool in the Turkish population: Comparison with Braden QD-T.

Unraveling the sensory metabolome of blueberries: An integrated metabolomics and machine learning approach across cultivars and geographical origins.

Peptidomics and molecular docking reveal digestion-resistant IgE-binding epitopes in bovine β-lactoglobulin and α-lactalbumin from simulated infant digestion.

Active regulation of droplet division in microfluidic chips: multi-physics coupled model prediction and high-throughput experimental validation

AlphaLexChinese: Measuring lexical complexity in Chinese texts and its predictive validity for L2 writing scores

Artificial intelligence (AI) is transforming pharmacology, drug safety, and toxicology by accelerating the drug development process to be more efficient, precise, and economical. Conventional drug discovery, pre-clinical testing, and post-marketing surveillance methods frequently encounter high costs, long lead times, ethical constraints, and low predictive validity in human outcomes. Utilizing machine learning (ML) and deep learning (DL), AI combines heterogenous datasets chemical structures, genomics, clinical data, and imaging to bridge these gaps.In drug design and discovery, AI has hastened predictions of protein and RNA structures (e.g., AlphaFold), enhanced virtual screening, and enabled de novo drug design with generative models. It has also hastened peptide-based drug development and improved pharmacokinetic prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) and reduced failure rates.

A novel strategy based on mouse organoid biosensor for detecting umami substances and their synergistic effect.

Purpose:To evaluate if prostate-specific antigen density (PSAD) predicts incidental prostate cancer (iPCa) in patients undergoing robot-assisted simple prostatectomy (RASP) for benign prostatic hyperplasia (BPH).Methods:A total of 100 consecutive patients undergoing RASP for BPH were analyzed. Patients were stratified into low-risk and higher-risk groups based on their iPCa risk: 60 patients (PSAD ⩽ 0.1 ng/mL/cc) and 40 patients (PSAD > 0.1 ng/mL/cc), respectively. Outcomes included iPCa detection rates, preoperative imaging/biopsy utilization, and postoperative complications. A multivariable logistic regression and an univariate linear regression analysis were conducted to assess whether PSAD can predict the incidence of PCA.Results:iPCa was detected in 8% of cases. Five patients had <5% tumor material in their final pathology (pT1a), while three had more than 5% (pT1b). iPCa was detected in eight patients, six with International Society of Urological Pathology (ISUP) 1 and 2 with ISUP > 2. Patients with ISUP 1 were managed with active surveillance; only one chose robot-assisted radical prostatectomy, and the two with ISUP 2 and 3 opted for external radiation. Seven iPCa cases occurred in the low-PSAD group (11.7%), and one in the high-PSAD group (2.5%). In multivariate logistic regression, only a prior negative prostate biopsy was the strongest predictor of iPCa (odds ratio = 5.2, p = 0.01). PSAD > 0.1 ng/mL/cc was not associated (p = 0.09). A univariate linear regression using PSAD as a continuous variable showed no significant association (p = 0.27).Conclusion:PSAD, whether dichotomized (threshold of >0.1 ng/mL/cc) or continuous, didn’t predict iPCa in men with large prostates. To optimize cancer detection, patients with large prostates may profit from prostate MRI before bladder outlet surgery, especially those with a history of prior prostate biopsy. Further research, including larger multicenter studies, is needed to validate our results.

The Big Breeding Innovation Team (Big BIT) maize (Zea mays L.) experiment was one of the largest genomic data‐informed predictive breeding validation studies ever conducted. The experiment was a multi‐location, multi‐year, multi‐tester, multi‐population study involving F1 maize hybrids created by crossing individual doubled haploids to inbred testers. The purpose of the study, performed by DuPont Pioneer/Corteva Agriscience in 2017, 2018, and 2019, was to build comprehensive datasets to help answer a wide range of practical questions focused on optimizing predictive breeding strategies in maize. The purpose of our study is to (1) describe the design and unique features of our study and (2) discuss learnings with practical implications for plant breeders. Since the same F1 maize hybrids were grown across three distinct years, we use basic descriptive summary statistics to discuss our learnings. We provide a technical justification for the use of basic statistics and discuss the expected theoretical prediction accuracy of genomic estimated breeding values (GEBVs) of Big BIT individuals and families, and predictive abilities obtained by performing large‐scale cross‐validations. Our study provides multi‐year field data‐based evidence that, for inbred/variety development focused plant improvement efforts, early‐stage genetic evaluation should be based on GEBVs generated from wide‐area testing training datasets. This holds true for candidates for selection with or without own phenotypic records.

Purpose of the study: This study investigates the predictive validity of peer assessment of teacher evaluations in English micro-teaching performance among preservice teachers Methodology: This study used a quantitative correlational-predictive design with 48 preservice teachers selected through random cluster sampling. The study used peer and teacher performance assessment rubrics covering eight teaching skills, which were previously validated by two experts (CVI = 1.0). Data were analyzed using Pearson correlation, linear regression, and paired-sample t-tests to examine predictive validity, alignment, and discrepancies between peer and teacher evaluations in micro-teaching performance. Main Findings: Data reveal a moderate to strong correlation between peer and teacher scores (r = 0.645, p &lt; 0.001), with peer assessments significantly predicting teacher evaluations (R² = 0.416). However, peer scores were consistently lower (M = 34.02 vs. 38.33, p &lt; 0.001), particularly in complex areas like classroom management and reinforcement. This highlights peer assessment’s value as a supplementary tool for evaluating teaching and fostering reflection, while underscoring the need for assessor training and rubric calibration to ensure reliability. Novelty/Originality of this study: This study brings a new perspective by exploring whether peer assessment in English micro-teaching can actually predict teacher evaluations. Unlike most research that sees peer review only as a learning aid, this study shows peers can meaningfully mirror teacher judgments, while also revealing where their views differ. The findings highlight the potential of peer assessment as both a learning and an evaluative tool in teacher education.

The reliability and validity of preclinical anxiety testing is essential for translating animal research into clinical use. However, the commonly used anxiety tests lack inter-test correlations and face challenges with repeatability. While translational animal research should be able to capture stable individual anxiety traits - the core feature of anxiety disorders - the conventional approach employs a single type of test at a single time, which primarily reflects transient states of animals that are heavily influenced by experimental conditions. Here, we propose a validated, optimized test battery capable of reliably capturing trait anxiety in rats and mice of both sexes. Instead of developing novel tests, we combined widely used tests (elevated plus-maze, open field and light-dark test) to provide instantly applicable adjustments for better predictive validity. We repeated these tests three times to capture behavior across multiple challenges, which we combined to generate summary measures (SuMs). Our approach resolved inter-test correlation issues and provided better predictions for subsequent outcomes under more anxiogenic conditions or fear conditioning. SuMs were also shown to be more sensitive markers of stress-induced anxiety following social isolation. Finally, we tested our method’s efficacy in discovering anxiety-related molecular pathways through RNA sequencing of the medial prefrontal cortex. SuMs revealed four-times more molecular correlates of trait anxiety than transient states, highlighting novel gene clusters. Furthermore, 16% of these correlates were also found in the amygdala. In summary, we provide a novel approach to capture trait anxiety in rodents, offering improved predictions for potential therapeutic targets for personalized medicine. We also provide recommendations to enhance feasibility without compromising validity or animal ethics, tailored to various scientific goals.

This study aims to analyze the risk factors for acute pain after percutaneous vertebroplasty in patients with thoracolumbar spine fracture and create a predictive model for validation. Clinical data of thoracolumbar spine fracture patients admitted to our hospital from January 2023 to December 2024 were retrospectively collected, and the visual analog score was used to assess the pain within 48 hours after the operation, and a visual analog score of >3 was defined as acute pain. Independent risk factors were screened by univariate and multivariate logistic regression analyses, and the model was visualized using a nomogram. The performance of the model was assessed by calculating the area under the curve from the receiver operating characteristic curve, and the model fit was verified using the Hosmer–Lemeshow goodness-of-fit test. To improve the reliability of the validation results, Bootstrap combined with 10-fold cross-validation was used for internal validation, and calibration curve and decision curve analyses were applied to assess the clinical utility of the model. Two hundred ninety-four patients were included, of which 186 (63.27%) experienced acute pain after surgery. Univariate and multifactorial logistic regression analyses showed that 5 independent risk factors were associated with acute postoperative pain: body mass index > 24 kg/m2 (odds ratio [OR], 1.834; 95% confidence interval [CI], 1.230–4.324), number of fractured vertebra > 1 (OR, 3.902; 95% CI. 1.873–9.423), unsatisfactory cement distribution (OR, 3.004; 95% CI, 1.483–6.837), vertebral compression height > 4 mm (OR, 3.319; 95% CI, 1.376–5.766), and fracture site in lumbar spine (OR, 1.457; 95% CI, 1.137–2.769). The occurrence of acute pain after percutaneous transluminal vertebroplasty in patients with thoracolumbar spine fracture is associated with a variety of factors, and the prediction model constructed in this study has good prediction accuracy, which can help to identify high-risk patients at an early stage and intervene.

Immigrant survivors of intimate partner violence (IPV) face particular risks and have unique strengths; IPV risk assessments must account for diverse lived experiences. This validation study of the Danger Assessment for Immigrant (DA-I) women assessed risk factors and experiences of IPV across four timepoints among immigrant IPV survivors from diverse world regions (n = 122). The Receiver Operating Characteristic Area Under the Curve assessed the predictive validity of the DA-I, which ranged from .794 to .892 in the full sample and .652-.943 in regional subsamples. Used appropriately, the DA-I offers survivors an opportunity to make knowledgeable and empowered decisions regarding their safety.

The aim of this study was to develop a dynamic factorial model for predicting amino acid requirements in Hy-Line Gray laying hens during critical early growth stages (0–84 days), addressing the need for precision feeding in modern poultry production systems. Methods: Four sequential trials were conducted. In Trial 1, growth curves and protein deposition equations were developed based on fortnightly body composition analyses, with parameters evaluated using the Akaike and Bayesian information criteria (AIC and BIC). In Trial 2, the carcass and feather amino acid profiles were characterized via HPLC. And established the amino acid composition patterns of chicken feather protein and carcass protein (AAF and AAC). In Trial 3, maintenance requirements were quantified through nitrogen balance studies, and in Trial 4, amino acid patterns of feather protein (APD) and apparent protein digestibility (ADD) were established using an endogenous indicator method. These datasets were integrated through factorial modeling to predict age-specific nutrient demands. Results: The developed model revealed the following quantitative requirements (g/day) for 18 amino acids across developmental stages: aspartic acid (0.1–0.863), glutamic acid (0.170–1.503), serine (0.143–0.806), arginine (0.165–0.891), glycine (0.258–1.279), threonine (0.095–0.507), proline (0.253–1.207), alanine (0.131–0.718), valine (0.144–0.737), methionine (0.023–0.124), cysteine (0.102–0.682), isoleucine (0.086–0.458), leucine (0.209–1.067), phenylalanine (0.086–0.464), histidine (0.024–0.133), lysine (0.080–0.462), tyrosine (0.050–0.283), and tryptophan (0.011–0.060). The model demonstrated strong predictive validity throughout the 12-week growth period. Conclusion: This integrative approach yielded the first dynamic requirement model for Hy-Line Gray layers during early development. The factorial framework enables precise adjustment of amino acid provisions to match changing physiological needs and has high potential value in optimizing feed efficiency and supporting sustainable layer production practices.

Rating scales as predictors of speech perception in paediatric cochlear implant users.