Search Results (1,686)

Pediatric health research must translate evidence into decisions that affect children’s development, safety, function, and long-term well-being. Scientific progress can still fail in two opposing ways: claims may exceed their evidentiary support, or research programs may remain productive while repeatedly refining an established signal without resolving decision-relevant uncertainty. These risks are amplified by developmental heterogeneity, ethical constraints, surrogate or short-term outcomes, long follow-up, and caregiver-mediated implementation. This concept paper formalizes these failures as Evidence Drift (ED) and Causal Maturation Drift (CMD). ED is a claim-level failure in which a finding moves into a stronger or different inferential domain without an adequate bridge. CMD is a trajectory-level failure in which research continues to accumulate within an established domain after a signal is sufficiently characterized, without proportionate progression toward temporal, causal, comparative, long-term, or implementation evidence. Inferential Fidelity is proposed as the governing principle linking claim calibration to purposeful uncertainty reduction. Applications are illustrated through early childhood caries microbiome research, vitamin D and childhood caries, silver diamine fluoride, pediatric biomarker and omics pipelines, and artificial intelligence prediction studies. Two preliminary eight-item appraisal frameworks are introduced: the Evidence Drift Assessment Scale (EDAS) for claims and the Causal Maturation Drift Assessment Scale (CMDAS) for literature trajectories. These formative frameworks prioritize item-level profiles; any standardized index is secondary and non-diagnostic. A phased validation program includes content validation, cognitive testing, multi-assessor reliability, hypothesis-based construct testing, bibliometric trajectory mapping, and evaluation of practical utility. The framework offers investigators, reviewers, funders, guideline panels, and policymakers a structured approach to determining whether conclusions remain within evidentiary boundaries and whether research activity reduces the uncertainties that matter most to children and families. Transferability beyond pediatric research requires empirical testing. Full article

Oxidative imbalance can accompany physiologically demanding and environmental transitions in sheep, but its magnitude varies with tissue, production stage, diet, health status, and the biomarkers used. Nuclear factor erythroid 2-related factor 2 (Nrf2; encoded by NFE2L2) coordinates inducible cytoprotective responses through Kelch-like ECH-associated protein 1 (Keap1), antioxidant response elements (AREs), and Keap1-independent regulatory routes. This structured narrative review evaluates whether dietary antioxidants engage Nrf2-related responses in sheep and whether such responses translate into health, productivity, or product-quality benefits. Evidence was classified by model and endpoint. Tier 1 comprised ovine dietary interventions with tissue-level Nrf2-pathway measurements; Tier 2 comprised ovine dietary studies with redox or phenotypic outcomes but no pathway assay; Tiers 3 and 4 comprised other-ruminant and non-ruminant or in vitro mechanistic evidence, respectively. Only rutin in transition-period ewes and a water extract of Artemisia annua in lambs met Tier 1 criteria. Both altered Nrf2-related gene expression, but neither used pathway perturbation, DNA-binding assays, or definitive target-engagement methods; the results therefore indicate association rather than causality. Evidence for tannins, essential oils, vitamins, selenium, carotenoid-rich feeds, and other phytochemicals in sheep is broader for oxidative status and product stability than for Nrf2 activation. Growth, fertility, milk yield, and survival outcomes are heterogeneous and strongly context-dependent. Dietary antioxidants may act through direct radical interception, microbial metabolites, metal chelation, membrane protection, mitochondrial effects, receptor signaling, inflammation control, and, in some settings, Nrf2-related adaptation. Future studies should combine dose–response designs, exposure measurements, multiple redox markers, tissue-specific pathway assays, and causal validation. Accordingly, Nrf2 is a plausible mechanistic framework for sheep nutrition, but current evidence does not support broad causal or productivity claims. Full article

Background and Objectives: Real-world persistence with medication-supported weight management programs is often low. While digital weight loss services (DWLS) provide multi-modal digital supports to improve engagement and counter attrition, the existing literature frequently relies on unidimensional or binary classifications of user engagement. This study used unsupervised machine learning to identify distinct digital engagement phenotypes and evaluated their independent associations with 6-month weight loss outcomes in patients prescribed tirzepatide. Materials and Methods: This retrospective cohort study analyzed deidentified data from 9470 medication-adherent, complete-case adult patients (out of 39,220 tirzepatide initiators) within a British DWLS who initiated tirzepatide between 20 May 20 and 2 December 2025. K-means clustering was performed on four continuous, longitudinal usage metrics: weekly app logins, health coach messaging, automated assistant (JuneBot) messaging, and weight tracking. To evaluate the primary clinical endpoint—6-month percentage weight loss—unadjusted pairwise comparisons (Tukey HSD) and a fully adjusted ordinary least squares multivariate linear regression model were executed to control for baseline demographic, clinical, and interim behavioral covariates. Results: Four stable engagement phenotypes emerged: non-engaged (n = 1772), high health coach engagement (n = 1141), high JuneBot engagement (n = 1472), and passive self-trackers (n = 5085). In a baseline-adjusted multivariate regression model, all active phenotypes were independently associated with 6-month weight loss relative to the low engagement baseline. Compared with this reference group, adjusted mean differences in percentage weight loss were 4.60% (SE = 0.26, p < 0.001) in the high health coach cluster, 4.69% (SE = 0.24, p < 0.001) in the high JuneBot cluster, and 3.71% (SE = 0.19, p < 0.001) in the passive self-tracker cluster. Conclusions: In this selected per-protocol, complete-case cohort of patients who persisted with tirzepatide treatment and reported 6-month weight data, distinct patterns of digital engagement were associated with different weight-loss outcomes. Greater conversational and self-tracking engagement was associated with greater observed 6-month weight loss than low engagement after adjustment for measured baseline characteristics. However, the retrospective observational design, concurrent assessment of engagement and outcome, substantial cohort selection, and potential residual confounding preclude causal or comparative-effectiveness conclusions, including claims of equivalence between automated and human support. Prospective studies with temporally defined engagement exposures and randomized allocation to support modalities are required. Full article

Extra virgin olive oil (EVOO) is a key component of the Mediterranean diet and a functional food whose health properties are mainly attributed to phenolic compounds. Despite the availability of EFSA-approved health claims, consumers often rely on heuristic cues that may not correspond to scientifically recognized quality indicators. A cross-sectional online survey involving 516 Italian consumers was conducted to investigate consumption patterns, health-related beliefs, territorial identity, and sensory quality perception. Descriptive analyses, regional comparisons, and a multivariable logistic regression model predicting sensory misperception were performed. Most respondents reported frequent EVOO consumption and willingness to pay a premium for certified products. Territorial identity, trust in local producers, and origin-related cues emerged as important dimensions of quality perception. Although respondents generally recognized the health relevance of antioxidants and unsaturated fatty acids, misconceptions regarding sensory indicators were common. Specifically, 41% of respondents associated sweetness with EVOO quality, whereas bitterness and pungency were less frequently recognized. Greater perceived knowledge of EVOO health properties was associated with a lower likelihood of identifying sweetness as an indicator of EVOO quality. Overall, consumer evaluation of EVOO reflected the interaction of informational, symbolic, and experiential dimensions, highlighting the potential relevance of communication strategies and sensory education initiatives whose effectiveness should be evaluated in future research. Full article

Machine learning (ML) applied to pediatric intensive care data could enable earlier risk stratification and more precise decision support, but limited shareable pediatric datasets have constrained progress. The Pediatric Intensive Care database (PIC) on PhysioNet provides a de-identified, bilingual electronic health record resource spanning 2010–2018. In this narrative review we synthesize ten PIC-based ML studies identified through forward citation tracking on the original PIC publication and PhysioNet dataset record in PubMed, Web of Science, and Google Scholar. For each study we abstracted the clinical question, cohort, label construction, feature engineering, model class, validation design, calibration reporting, and release of reproducibility artifacts. The reviewed studies addressed catheter-associated thrombosis, sepsis, in-hospital mortality, and organ-dysfunction phenotyping. We organize the synthesis around four substrate properties of PIC: irregular time series, constructed labels, single-center temporal drift, and bilingual identifier semantics. The review advances three claims. First, upstream design choices appear to drive performance at least as much as classifier selection across the studies reviewed. Second, within-site discrimination metrics are insufficient without calibration, decision-curve analysis, and deployment-realistic validation. Third, PIC should be viewed as the seed for a collaborative pediatric ICU data ecosystem. Full article

The personal care product market has expanded rapidly in recent years, along with growing consumer awareness of chemical exposures and increasing demand for “clean” products. Consumer perceptions of product safety and potential health impacts are commonly based on ingredient labels, intended use, and certifications, rather than the full chemical composition of finished products. In this study, we conducted a targeted review of certification and ingredient-evaluation programs in the United States and European markets. Eighteen programs were identified and characterized based on their evaluation approaches, data sources, and whether they incorporate analytical measurement of finished products. We also evaluated the current U.S. and EU regulatory frameworks. Across both certification and regulatory systems, evaluation was found to rely primarily on ingredient-based approaches and supporting documentation, with limited incorporation of analytical measurement. As a result, contaminants, impurities, and incidentals/non-intentionally added substances (which have previously been identified in many consumer products) may not be consistently identified or evaluated for safety. These findings highlight a fundamental gap between intended formulation and actual product composition. Incorporating analytical measurement, particularly non-targeted approaches, provides a complementary groundwork for identifying previously unrecognized chemical exposures and improving the accuracy of product safety certification programs and hazard assessments. Aligning evaluation with measured chemical composition may enhance transparency and better reflect real-world exposure, support more credible sustainability claims, enhance consumer trust, support growing market demand, support consumer safety, and contribute to more effective regulation in the personal care industry. Full article

Intrinsically self-sensing cementitious composites offer a promising basis for continuous structural health monitoring. Their electrical response, however, is strongly affected by reversible moisture change and freeze–thaw exposure. This study presents a computational proof-of-concept for the AI-driven design of a federated digital-twin architecture for damage identification and adaptive sensing in sustainable self-sensing cementitious infrastructure. The framework is developed and evaluated entirely in software on a physics-based synthetic testbed. At its present maturity, it is therefore a digital-twin precursor rather than an operational digital twin: it has no calibrated physical counterpart and no live, two-way data coupling, and no experimental validation is claimed. A transparent, physics-based signal generator produces fractional-change-in-resistance signals for twelve virtual CNT/biochar-functionalized LC3 and geopolymer specimens. Each passes through four progressive damage stages interleaved with wet–dry and freeze–thaw conditioning. The framework integrates a CNN-LSTM damage classifier, unsupervised domain adaptation, federated learning, reinforcement-learning-based active sensing, and quantum-inspired aggregation optimization. On three unseen virtual specimens (654 evaluation windows), the CNN-LSTM achieved 70.3% four-stage accuracy (95% Wilson confidence interval 66.7–73.7%) and a macro-F1 score of 0.650, with per-specimen accuracy ranging from 63.8% to 77.1%. It reached 85.2% (95% CI 82.3–87.7%) for the damaged-versus-undamaged decision and reduced environment-induced false alarms by 74.7% (95% CI 61.7–83.4%) relative to a calibrated threshold detector. Federated averaging was less accurate and less stable than centralized training; the 5.2 percentage-point gain from quantum-inspired aggregation lies within the resolution of the evaluation set and is not established as a real improvement. The active-sensing controller reduced measurement cost by 98.9% but detected only four of 27 damage-progression events. All sensing data are synthetic, and every interval reported here is recomputed from the evaluation counts already reported rather than obtained from additional experiments. The results therefore establish algorithmic feasibility only and identify the components requiring refinement before experimental validation. Full article

Background/Objectives: Reproductive-health topics such as polycystic ovary syndrome (PCOS), endometriosis, fertility, and menstruation attract both unsupported claims and stigmatizing framing on social media. We present an evidence-grounded pipeline combining large language models (LLMs) with biomedical retrieval, extended with a novel layer that scores the stigma framing of each claim independently of its truth. Methods: From 991 keyword-sampled English-language Bluesky posts we extracted 937 health claims, grounded each in evidence from PubMed, openFDA, and authoritative clinical guidelines (ACOG, NICE, NHS, WHO), and classified veracity into five categories using only the retrieved evidence. A parallel module scored five stigma dimensions and, separately, empowerment as a counter-stigma indicator. Results: Most claims (75.2%) were non-supported, though a relevance audit shows this largely records evidence that was not retrieved rather than claims shown to be false; the dominant frame was empowerment rather than overt shame. Stigma increased monotonically as claims departed from the evidence. The gradient was modest but robust (Kruskal–Wallis $p < {10}^{-7}$, ${\epsilon }^2 = 0.039$; $\rho = 0.20$), surviving cluster-aware reanalysis of claims nested within posts. Stigma varied sharply by condition, highest for infertility and endometriosis. Conclusions: Non-supported claims and stigmatizing framing co-occur, though this cross-sectional design cannot establish a direction. Single-coder validation was a pilot diagnostic check: stigma scores aligned directionally with human judgment, while five-way veracity agreement was limited. Results describe this keyword-sampled, single-platform corpus, not platform-wide prevalence. Full article

Background and Objectives: Rising malpractice litigation and the strengthening of patient-rights legislation have changed the way hospital staff approach informed consent and legal risk. This study aimed to (i) assess the distribution of informed-consent knowledge, malpractice anxiety, and defensive practice across professional and departmental groups; (ii) identify independent predictors of high defensive practice; and (iii) evaluate experience-related moderation of the legal-literacy–anxiety association and predictor consistency across defensive-practice severity. Methods: We conducted a cross-sectional survey of 146 healthcare workers (physicians, nurses, midwives, and allied health professionals) from four departments of a tertiary academic hospital network, using a 46-item pilot-tested instrument measuring informed-consent knowledge (0–20), malpractice anxiety (0–100), and defensive-practice behaviors. Results: Physicians showed higher informed-consent knowledge than nurses (14.1 vs. 12.0 points, p < 0.001) but also higher malpractice anxiety (63.4 vs. 57.6, p = 0.015). High defensive practice was associated with a prior malpractice claim (adjusted odds ratio 3.24), surgical specialty (2.47), low consent literacy (2.21), physician role (2.10), and knowing a sued colleague (1.88). Anxiety was highest in emergency medicine; predictors also operated across ordered defensive-practice severity, and the legal-literacy–anxiety association was stronger at lower experience (interaction p = 0.023). Conclusions: Knowledge, anxiety, and defensive practice were unevenly distributed; defensive practice reflected professional context and direct or vicarious litigation exposure, while the legal-literacy–anxiety association varied with experience. Medico-legal education may therefore be best paired with structured consent procedures and experience-sensitive support; larger multi-center studies are needed. Full article

Despite Lyme disease’s (LD) status as the most common vector borne disease in the United States, relatively little attention has been paid to the demographic characteristics of LD patients or health disparities among them. Using administrative claims for Medicare and Medicaid, two large public health insurance programs, we calculate the incidence of LD within strata of a variety of social determinants of health (SDoH) among beneficiaries living in high incidence LD states. Our findings focus principally on two critical causes of disparities, socioeconomic status and racial residential segregation. We find that distinct patterns in LD incidence exist across these strata for both Medicare and Medicaid beneficiaries who had LD between 2016 and 2021. LD rates are highest in areas with low levels of socioeconomic inequality and racial residential segregation. When positioned against other data in the literature, we argue that these findings may reflect access to health care or inadequate recognition of LD symptoms for patients from particular communities. Full article

Vitamin D is a fat-soluble vitamin, considered a prohormone, that plays a fundamental role in calcium absorption and reabsorption, as well as the modulation of inflammatory response in infectious conditions such as COVID-19. A literature search was conducted in scientific databases and official sources, selecting studies published in the last 10 years in Spanish and English related to vitamin D and COVID-19; studies unrelated to the nutritional approach were excluded to reduce bias and ensure a rigorous and reproducible analysis. It was observed that the probability of contracting COVID-19 increases as the D3 hypovitaminosis prevalence increases. In Mexico, the relevance of vitamin D deficiency or supplementation in this disease has been controversial. Some studies claim that vitamin D supplementation could be a protective factor. According to the National Health Survey in Mexico (Ensanut), the Mexican population has significant deficiencies in this vitamin, contributing to an unfavorable diagnosis, particularly for children and pregnant women, as it is considered a deficiency in the blood when it is below 30 ng/mL and insufficiency when below 20 ng/mL, making these deficiencies important risk factors in the development of COVID-19 severe cases. Nevertheless, the use of vitamin D as an adjuvant agent in the treatment of COVID-19 can represent one of the main options for public health, providing therapeutic properties and health benefits. Full article

Autonomous vehicles depend on heterogeneous sensing systems whose performance varies with range, illumination, weather, object material, traffic geometry, contamination, calibration quality, and cyber-physical interference. This PRISMA 2020 and PRISMA-S systematic review synthesized 65 peer-reviewed primary studies selected from 2143 records identified through four databases. After removal of 793 records before screening, 1350 titles and abstracts were screened; 273 full texts were assessed and 208 were excluded with documented reasons. The final evidence base covers cameras, LiDAR, radar, thermal and event cameras, GNSS/IMU localization, calibration, synchronization, multimodal fusion, adverse-weather perception, sensor-health monitoring, and fault-tolerant perception. No modality was universally superior: comparative performance depended on hardware generation, dataset, environmental severity, range, and metric. Direct evidence was strongest for component-level perception and controlled degradation, whereas health-conditioned fusion, ODD restriction, and minimum-risk behavior were supported mainly by partial experimental evidence and safety-oriented synthesis. The review therefore proposes, rather than claims to validate, a reliability-aware architecture that separates sensor health from task confidence, preserves uncertainty and provenance, adapts fusion, and constrains operation when residual evidence is insufficient. The review was retrospectively registered in PROSPERO on 30 July 2026 (CRD420261465869). Full article

Objective: This study investigates the relationship between social media use and consumer purchase intentions or out-of-home food choices in the Polish gastronomic market, focusing specifically on variations across generations. Methodology: Data were collected between May and July 2024 through a diagnostic survey using the Computer-Assisted Web Interviewing (CAWI) technique (N = 1099). Respondents were recruited via a non-probability quota sampling approach based on strict demographic inclusion criteria. To test the research hypotheses regarding generational variations in market behaviour, a multivariate discriminant function analysis was performed using Statistica 13.1 PL. A preliminary pilot study (N = 30) confirmed the initial questionnaire readability and overall consistency (Cronbach’s alpha = 0.87), while subscales were treated independently during the main analysis. Results: Multivariate models were found to be highly significant, revealing clear differences between age groups. The youngest cohort (aged 18–35) relies heavily on Instagram and TikTok, showing distinct patterns regarding visual triggers such as “instagrammable” aesthetics, digital validation, and menu uniqueness alongside weight loss claims. Conversely, seniors (aged 61 and older) lean unexpectedly towards X. This older segment displays pragmatic, utility-driven motives, searching for detailed textual data about ingredients and the health-promoting properties of food. General product quality and calorie control were identified as universal factors that do not vary by generation. Conclusions and Managerial Implications: The digital transformation of the Polish restaurant industry does not follow a single path. Social media is closely linked to modern customer journeys, with physical dining spots frequently serving as spaces for socialisation. Consequently, restaurant operators should move away from mass communication and adopt a selective omnichannel strategy, where message formats shift from visual appeal to factual, nutrition-oriented text that is tailored to the digital literacy and dietary needs of each generation. Full article

Background/Objectives: On 1 November 2023, the Medicare Benefits Schedule (MBS) introduced major reforms to prostate-specific antigen (PSA) testing in Australia, extending the minimum re-test interval for average-risk men (item 66655) from 12 to 23 months and introducing a new annual high-risk item (66654). These changes were aimed at reducing overscreening, aligning practice with national guidelines, and improving cost efficiency; however, their real-world impact on PSA utilisation, downstream diagnostic activity, and health-system costs has not been evaluated. Methods: We performed a national, population-level analysis of all reimbursed PSA tests (items 66655 and 66654) and prostate magnetic resonance imaging (MRI) scans (item 63541) from November 2021 to October 2025 using MBS claims data. Monthly utilisation trends were examined descriptively and modelled using interrupted time-series segmented regression with Newey–West heteroscedasticity- and autocorrelation-consistent standard errors. Economic impacts were estimated using schedule fees. MRI trends were assessed to explore early signals of downstream diagnostic change. Results: Prior to the reform, average-risk PSA testing (item 66655) averaged 61,704 claims per month (median 62,526). After November 2023, utilisation fell immediately to 49,620 and stabilised at a lower mean of 44,702 claims per month (median 43,495), representing a sustained 27.6 percent reduction. High-risk PSA testing (item 66654) accounted for 130,739 claims post-reform (10.9 percent of monthly post-reform PSA activity), but the increase in high-risk testing did not offset the reduction in average-risk claims; total PSA claims fell from 61,704 to 50,149 per month (−18.7 percent). Interrupted time-series modelling confirmed a large, significant step decrease for item 66655 (−23,191 claims, 95% confidence interval (CI) −31,215 to −15,167; p < 0.001) and for total PSA testing (−16,876 claims, 95% CI −25,899 to −7853; p < 0.001). In contrast, prostate MRI utilisation increased from 3854 to 4454 monthly scans (+15.6 percent) with no significant step-change at the time of the reform (p = 0.74) but a significant acceleration in post-reform growth (+49 scans/month, p = 0.0095). The reform resulted in an estimated net saving of $2.8 million in annual PSA expenditure. Conclusions: The November 2023 MBS reforms led to a large, immediate, and durable reduction in reimbursed PSA screening among average-risk men, while high-risk testing increased modestly but remained proportionally small. Total PSA testing declined meaningfully, producing substantial cost savings for Medicare. Rising MRI utilisation indicates that downstream diagnostic activity did not fall in parallel with reimbursed PSA claims, though this trend may reflect independent drivers of MRI growth rather than preserved screening vigilance. Reductions varied by jurisdiction, with two smaller jurisdictions showing net increases over the study window, underscoring that the national pattern was not uniform. Full article

Background/Objectives: Obesity is associated with respiratory infection risk, but its relationship with post-discharge healthcare burden remains unclear. This study examined the associations of body mass index (BMI) and waist circumference (WC), evaluated separately, with hospitalization and post-discharge outcomes in analyses stratified by sex among South Korean adults with claims-defined hospitalizations involving influenza or pneumonia. Methods: This retrospective cohort study used the 2018 National Health Insurance Service (NHIS) Customized Research Database linked to health-screening data. Adults aged 20–89 years with claims-defined hospitalizations involving influenza or pneumonia were included if BMI and WC measurements were available from the most recent NHIS health-screening examination conducted within two years before the index admission. Length of stay and index hospitalization costs were summarized descriptively. Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause and pneumonia-related readmission within two years after discharge, and two-part models were used to estimate readmission-related costs, expressed in US dollars (USD). Results: The cohort included 175,774 patients, of whom 80,257 (45.7%) were male. Category-specific associations varied according to the anthropometric measure, outcome, and sex stratum. In female patients, the highest WC category had the largest estimated all-cause readmission HR (HR 1.195, 95% confidence interval 1.157–1.235), and the highest model-based mean cost of the first all-cause readmission was also observed in this category (USD 1407.4). In male patients, several higher BMI categories had lower estimated readmission HRs than the reference category; however, these estimates should not be interpreted as protective effects because competing mortality was not accounted for using competing-risk methods. Patterns for pneumonia-related readmission and costs were less consistent. Conclusions: BMI and WC showed category-specific associations with readmission, while model-based mean readmission costs varied across anthropometric categories and sex strata. Higher WC categories were associated with higher all-cause readmission HRs in the female stratum; however, the independent or incremental prognostic value of BMI and WC was not evaluated. Full article