Traditional credentials—degrees, certifications, and job titles—are losing their predictive validity as sole indicators of workplace capability. Skills marketplaces are emerging as intermediary platforms that enable granular, competency-based matching between talent and opportunity, prioritizing demonstrated ability over institutional gatekeeping. This article synthesizes evidence from organizational psychology, labor economics, and human capital development to examine the organizational and individual consequences of credential inflation, signal degradation, and access inequality. It outlines evidence-based organizational responses including competency-based assessment infrastructure, transparent skill taxonomies, and equitable validation pathways. The transition from static credentials to dynamic capability verification represents not merely a technological shift but a fundamental renegotiation of the psychological contract between employers, workers, and educational institutions. Organizations adopting capability-centered approaches demonstrate improved talent identification, deployment efficiency, and workforce diversity while navigating complex challenges in assessment validity, privacy protection, and equitable access.

The Leuven Exeter Dampening Scale (LEDS) to measure dampening appraisals towards positive affect: Psychometric evaluation and initial validation in a Dutch and English community sample.

Identifying preventable readmissions for healthcare systems facing significant financial strain from rehospitalizations is crucial. The Hospital score, recognized for its predictive power in diverse settings, has yet to be extensively validated in low-income countries, particularly amid the challenges posed by the COVID-19 pandemic. This study validates the Hospital score for predicting 30-day readmission in a tertiary private hospital in Southern Brazil, assessing its efficacy in a middle-income country context. We retrospectively analyzed adult medical discharges from Hospital Moinhos de Vento between January 2019 and December 2023. The study focus was evaluating the Hospital score's accuracy in forecasting 30-day readmissions, including a specific assessment of its performance among COVID-19 patients during the pandemic years. Data extraction from electronic health records facilitated a comprehensive analysis of score variables and patient outcomes. Among 69 971 discharges, 2890 patients were readmitted within 30 days, marking a 4.3% readmission rate. The Hospital score demonstrated consistent predictive accuracy, with an overall score accuracy of 0.70 (95% CI: 0.66-0.73; p < 0.01) and 0.72 (95% CI: 0.64-0.75; p < 0.01) among COVID-19 patients. Notably, the pandemic did not significantly impact the score's predictive validity. The Hospital score remains a reliable predictor of 30-day readmissions in a tertiary hospital within a middle-income country, retaining its effectiveness amidst the COVID-19 pandemic. These findings underscore the score's potential as a universal tool for reducing preventable readmissions, highlighting its relevance across healthcare contexts.

Ice loads in ship-ice glancing impacts: Experimental investigation and validation of energy-based predictions

Prediction and Experimental Validation of Temperature-Time Coupled Compression Recovery Performance in High-Temperature Seals

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

Umami-Transformer: A deep learning framework for high-precision prediction and experimental validation of umami peptides.

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

Early assessment of the risk of child abuse in well-child care: The predictive validity of a family-centered approach

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.

Predictive validity of chronic obstructive pulmonary disease phenotypes in inpatient elective surgery: a population-based study.

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.

Simple SummaryThis study advances the field of poultry nutrition by introducing a dynamic modeling approach to estimate amino acid requirements for layer chicks, addressing limitations in traditional static models. This approach aligns with recent trends advocating for precision nutrition in poultry farming, where tailored feeding strategies optimize both growth efficiency and cost-effectiveness. Positioned within the literature, this research enhances the toolkit for precision livestock nutrition, offering a scalable framework applicable to other poultry species or phases. Its emphasis on adaptability and biological relevance addresses critical challenges identified in earlier studies, such as the oversimplification of nutrient requirements in heterogeneous populations.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.