The gold standard for establishing the causal effect of a clinical treatment is a randomized controlled trial, which is challenging to apply in orthopaedics, mainly due to ethical reasons. Therefore, retrospective observational studies were conducted to elucidate potential treatment effects. However, clinicians rarely receive guidance on how to apply modern causal inference methods to strengthen conclusions drawn from such data and support evidence-based decision-making. The objective of this scoping review was to facilitate a better understanding of the use of causal inference methods in orthopaedics. The scoping review adheres to the Joanna Briggs Institute methodology and the five-stage framework of Arksey and O'Malley. Two themes, 'orthopaedic interventions' and 'causal inference', guided the search strategy development for this scoping review. A literature search of observational studies was conducted using the PubMed, Cochrane Central Register of Controlled Trials, EMBASE, and SCOPUS databases. Studies describing the use of causal inference frameworks in clinical treatment in orthopaedics were included. Of 6,244 records, 85 studies were included in the study. Propensity score matching and inverse probability of treatment weighting were the most common methods. Multicentric studies predominated, while the use of formal causal inference frameworks, such as target trial emulation and directed acyclic graphs, was rare. Causal inference is increasingly used in orthopaedics, although formal frameworks, such as target trial emulation and causal diagrams, remain underutilized. We recommend using prospective, publicly available protocols that incorporate these tools to enhance transparency and validity.

Machine learning is transforming the grape and wine industry by shifting traditional experience-driven practices toward data-driven and intelligent decision making across the entire value chain. This review provides a conceptually driven synthesis of machine learning-enabled technologies spanning vineyard sensing, precision viticulture, fermentation monitoring, and winemaking optimization. An integrated analytical framework is proposed, linking multisource data acquisition, preprocessing and representation learning, model development, and intelligent decision making into a unified end-to-end pipeline. Building on this framework, the review critically examines advances related to five key scientific challenges: multimodal data integration, model interpretability, cross-domain generalization, whole-chain decision coordination, and scalable industrial deployment. Particular emphasis is placed on the mechanisms and trade-offs of multiscale sensing technologies (including spectroscopy, chromatography-mass spectrometry, imaging, and electronic sensing), as well as data preprocessing, feature engineering, and multimodal fusion strategies. A task-oriented and data-structured perspective on model selection is highlighted, covering linear models, kernel methods, ensemble learning, deep neural networks, and probabilistic frameworks, alongside evaluation protocols and interpretability approaches. In contrast to fragmented task-specific studies, this review highlights the importance of cross-stage integration and closed-loop decision systems linking vineyard management with downstream vinification and quality evaluation. Despite rapid progress, key challenges remain, including data scarcity and heterogeneity, limited model transferability, and high implementation costs. Emerging directions such as knowledge-guided machine learning, causal inference, small-sample learning, and human-AI collaboration are expected to enhance robustness and interpretability. Overall, this review provides a structured roadmap for advancing intelligent and sustainable practices in the grape and wine industry.

Covariance-intersection-fusion-based causal inference under missing data

To assess the perceived effectiveness of the school nurse services through students' responses and to examine whether there is a detectable relationship between the students' subjective health status and the school nurse's service delivery. We conducted our cross-sectional, quantitative research in the West Transdanubian region of Hungary using convenience sampling among high school students (n = 429). Given the non-probability sampling, the findings may not be generalizable to all student populations. Data were collected using standardized (SF-36, EQ-VAS, questions on the prevalence of subjective health complaints from the HBSC survey) and a self-developed questionnaire. Chi-square tests, correlation analyses and multivariable linear regression analyses were performed using SPSS Statistics 27.0 software. Students reported a higher level of health-related knowledge received from the school nurse, along with better perceived applicability of this knowledge in everyday life (p < 0.05). Nearly three quarters of respondents indicated that the school nurse's activities contributed to the development of their health-related attitudes and this perception showed a statistically significant association with both the quantitative and qualitative evaluation of the care provided (p < 0.05). Higher ratings of the perceived effectiveness of school nursing activities were associated with more favourable self-rated mental health and a lower level of reported health complaints; however, these associations were weak in magnitude. The results revealed statistically significant but weak associations between perceived school nurse services and selected indicators of students' self-rated health status. Due to the cross-sectional design and the use of self-reported data, causal inferences cannot be drawn. Nonetheless, the findings suggest that more favourable quantitative and qualitative evaluations of the care provided are linked to higher perceived effectiveness of school nurse activities. Further research on quality indicators of school nurse services and approaches tailored to students' needs may be warranted. The observed associations between students' perceptions of school nurse services and their health perceptions underscore the importance of continuous professional development for school nurses. This may include advanced training in health education and communication skills to further support the perceived effectiveness of their activities. What problem did the study address? We addressed the perceived effectiveness of school nurse services from the perspective of students and examined whether there is a detectable relationship between students' subjective health status and the services provided by school nurses. What were the main findings? The study found that higher levels of health-related knowledge reported as being provided by the school nurse were associated with more favourable student ratings of the applicability of this knowledge in everyday life. Students who reported more positive experiences with school nurse activities also tended to report more favourable health perceptions, which were reflected in the indicators used to evaluate the services. In addition, higher perceived effectiveness of school nurse services was associated with more favourable self-reported mental health and vitality, as well as a lower level of reported health complaints. Where and on whom will the research have an impact? The findings may be particularly relevant for school nurses, as they highlight the importance of their role and the potential need for continuous attention to service quality from the students' perspective. They may also be informative for students and school communities by underscoring that health education and services provided by school nurses are associated with more favourable self-reported health perceptions and overall well-being. No patients or members of the public were involved in the design, conduct, analysis, or reporting of this study beyond students' participation as anonymous questionnaire respondents.

Network threats to causal inference: Variations in network position by participation in randomized controlled trials.

Artificial intelligence (AI) holds transformative potential for advancing oral health surveillance by streamlining data collection, integration, and dissemination. This review critically synthesizes AI applications in oral health surveillance, highlighting its roles in 1) mapping population-level trends and oral health inequities using machine learning on epidemiological data; 2) enabling remote screening of oral diseases/conditions, including caries, oral hygiene, gingivitis, oral cancer, and malocclusion from intraoral images via computer vision models; and 3) integrating multimodal data through emerging large language models (LLMs) to enhance precision public health. We clarify the comparative strengths of distinct AI modeling for processing the primary data types in surveillance: structured clinical records, unstructured images, and integrated multimodal data. Traditional machine learning methods have been effectively applied to map population-level oral health disparities and identify risk factors but are constrained to structured data. Computer vision methods excel in individual-level diagnostics using intraoral photographs. To translate such capability into scalable surveillance, it is recommended to establish standardized imaging protocols for nonclinical settings, develop scalable models for fine-grained feature extraction, and implement reliable evaluation. These steps are essential to address pervasive challenges, including inconsistent image quality, domain shift, prevalence imbalance, and cost-effectiveness constraints. The future of AI-driven oral health surveillance lies in developing dental-adapted multimodal LLMs (MLLMs). Such MLLMs are uniquely capable of synthesizing disparate data streams, from structured clinical data to heterogeneous imaging modalities (e.g., intraoral photographs and radiographs), or even biomolecular data. This integration capacity facilitates a paradigm shift, moving current applications in dental consultation and clinical decision support toward a novel, tiered system for population-level monitoring. Such a system would provide actionable insights for public health policymaking via spatiotemporal analysis and causal inference. Next-generation AI-driven oral health surveillance systems can only succeed when built on a strong foundation of rigorous ethical principles and safeguards.

The tumor microenvironment is increasingly recognized as a complex ecosystem in which the intra-tumoral microbiota (comprising bacteria, fungi, viruses, etc.) plays a pivotal, underappreciated role in cancer biology. This review systematically summarizes the latest research into the origins, diversity, and functional mechanisms of intra-tumoral microbiota, emphasizing their dual roles in tumor formation, progression, and response to treatment. Using high-throughput sequencing, spatial multi-omics, and integrative bioinformatics, we identify the multifactorial origins of tumor-resident microbiota, including translocation from adjacent tissues, hematogenous dissemination, and viral genomic integration. We also examine the significant inter- and intra-tumoral microbial heterogeneity influenced by anatomical, environmental, and immunological factors, and the tissue-specific functional effects of important microbial species. We detail the mechanisms by which the intra-tumoral microbiota modulate innate and adaptive immunity through pattern recognition receptor signaling, microbial antigen presentation, and microbial metabolite production, ultimately influencing tumor microenvironment composition, immune cell dynamics, and therapeutic efficacy. Finally, we critically evaluate emerging microbiota-targeted therapeutic strategies, including engineered bacteria, antibiotics, bacteriophages, and oncolytic viruses, while outlining the technical, mechanistic, and regulatory challenges that hinder their clinical translation. Our synthesis highlights the need for rigorous multi-omics profiling, causal inference, and smart delivery systems to exploit the intra-tumoral microbiota for precision oncology. This paradigm shift offers unprecedented opportunities for personalized diagnosis and therapy, marking a new frontier in cancer research and treatment.

Estimating causal effects in quasi-experiments with spatio-temporal panel data often requires adjusting for unmeasured confounding that varies across space and time. Gaussian processes (GPs) offer a flexible, nonparametric modeling approach that can account for such complex dependencies through carefully chosen covariance kernels. In this paper, we provide a practical and interpretable framework for applying GPs to causal inference in panel data settings. We demonstrate how GPs generalize popular methods such as synthetic control and vertical regression, and we show that the GP posterior mean can be represented as a weighted average of observed outcomes, where the weights reflect spatial and temporal similarity. To support applied use, we explore how different kernel choices impact both estimation performance and interpretability, offering guidance for selecting between separable and nonseparable kernels. Through simulations and application to Hurricane Katrina mortality data, we illustrate how GP models can be used to estimate counterfactual outcomes and quantify treatment effects. All code and materials are made publicly available to support reproducibility and encourage adoption. Our results suggest that GPs are a promising and interpretable tool for addressing unmeasured spatio-temporal confounding in quasi-experimental studies.

Beta blockers, stroke, and noncardiac surgery: resolving an enduring perioperative dilemma.

Skeletal muscle relaxants (SMR), particularly baclofen, have been used in patients with dysfunctional voiding (DV) and poor relaxation of the external sphincter (PRES) with variable efficacy and notable side effects. Tizanidine, a centrally acting α2-adrenergic agonist, may offer comparable benefits with a more favorable tolerability profile. However, data regarding its use in DV remain limited. This retrospective analysis included adults diagnosed with DV/PRES between July 2017 and January 2025. Data comprised demographics, lower urinary tract symptoms (LUTS), urodynamic parameters, pre, and posttreatment uroflowmetry values, and subjective response to alpha-blockers with pelvic floor relaxation exercises, and subsequent addition of tizanidine. Uroflowmetry indices included Qmax, Qave, and volume-normalized flow indices (VQImax, VQIave). Clinical outcomes were assessed via patient-reported symptom improvement. Of 114 patients, 62.2% showed no response to alpha-blockers alone. Following addition of tizanidine (median dose 6 mg/day), 69.3% reported symptomatic improvement (45% ≥ 50% improvement, 24.6% < 50% improvement; p < 0.00001). VQImax improved by ≥ 50% in 52% of patients, and clinical improvement significantly correlated with changes in VQImax (p = 0.004) and VQIave (p = 0.009). Women demonstrated greater improvement in flow parameters than men (p < 0.05), although symptom relief was similar across sexes. Tizanidine was generally well tolerated, with sedation being the most common dose-limiting side effect (25%). Median follow up duration was 12 months (IQR 9-22). Addition of tizanidine to alpha-blocker and pelvic relaxation is associated with clinically significant improvement in LUTS and uroflometry parameters in adults with pelvic floor dysfunction. We acknowledge that causal inference is limited by the study design and randomized placebo-controlled studies are required for deriving stronger conclusions.

Second-order correlation learning in 2- to 4-year-old children, and its underlying mechanism.

How do traits and dispositional constructs relate to intentions and behavior? The trait-informed attitude, cognition, and intention tenet (TACIT).

Systematic elucidation of the cross-omics regulatory network in chronic rhinosinusitis: the LAT-IL23R metabolic axis.

Association between socioeconomic and health variables and community-acquired pneumonia mortality rates in Chile from 1990 to 2021.

Sedative-hypnotics, including benzodiazepines (BZDs) and non-benzodiazepine hypnotics (Z-drugs), are widely prescribed for insomnia and anxiety, particularly in older adults. Their long-term cognitive safety and potential association with Alzheimer's disease (AD) remain uncertain. We examined whether use of BZDs and Z-drugs is associated with incident AD and assessed variation by drug class, pharmacokinetics, and methodological factors. PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched from inception to 16 August 2025 without language restrictions. Reference lists of eligible articles and reviews were screened. We included observational cohort and nested case-control studies enrolling adults without dementia at baseline that compared BZD or Z-drug users with non-users and reported incident AD diagnosed using validated clinical or administrative criteria (e.g., ICD-9/10, NINCDS-ADRDA, or NIA-AA). We excluded reviews, case reports, conference abstracts, studies with overlapping populations, and studies without extractable effect estimates. Two reviewers independently screened studies, extracted data, and assessed risk of bias using ROBINS-E. Random-effects meta-analyses were performed separately for odds ratios (ORs) and hazard ratios (HRs). Heterogeneity was quantified with I2. Publication bias was evaluated with funnel plots and Egger test when applicable. Subgroup and meta-regression analyses assessed clinical and methodological modifiers. Certainty of evidence was rated using GRADE. The protocol was prospectively registered (PROSPERO CRD420251141623). Thirteen studies (N = 721,354 subjects) were included. Overall sedative-hypnotic use was associated with higher odds of AD (OR 1.29; 95% CI, 1.10-1.53; I2 = 86.5%). Estimates restricted to HRs were attenuated and not statistically significant (HR 1.17; 95% CI, 0.87-1.58; I2 = 73.1%). In subgroup analyses, BZDs overall (OR 1.21; 95% CI 1.07-1.36), Z-drugs (OR 1.14; 95% CI 1.10-1.18; I2 = 0%), and short-acting agents (OR 1.19; 95% CI 1.04-1.36) were associated with higher odds of AD, whereas broad-acting BZDs were not (OR 1.01; 95% CI 0.98-1.05). Long-acting agents showed a borderline estimate (OR 1.44; 95% CI 0.99-2.09). Age-stratified analyses showed higher odds in individuals aged <75 years (OR 1.36; 95% CI 1.24-1.49), but not in those aged ≥75 years (OR 1.14; 95% CI 0.61-2.11). Estimates were also higher in studies using ICD-based definitions (OR 1.47; 95% CI 1.16-1.86) than in those using clinical criteria (OR 1.13; 95% CI 0.84-1.52). Meta-regression identified drug class and publication year as significant moderators. Risk of bias was rated moderate to serious in several studies, mainly due to residual confounding and exposure misclassification. Certainty of evidence ranged from very low to moderate. Use of BZDs and Z-drugs was associated with increased odds of AD, with variation across drug classes and pharmacokinetic profiles. Short-acting agents, BZDs overall, and Z-drugs were associated with higher risk, whereas broad-acting BZDs were not; this finding should be interpreted with caution given subgroup heterogeneity and limited statistical power. Residual confounding and reverse causation limit causal inference. These results support careful prescribing and the need for prospective studies with detailed characterization of exposure, dose, duration, and clinical indication to clarify whether observed associations reflect drug-related effects or underlying disease processes. PROSPERO protocol number: CRD420251141623.

A range of environmental, lifestyle and biological exposures across the lifespan - varying in timing, duration and intensity - interact with genetic factors to shape an individual's neurocognitive phenotype. By referring to the totality of exposures that an individual has experienced in their life so far, the exposome offers a valuable concept to better understand interindividual variability in not only brain-behaviour phenotype but also vulnerability and resilience to brain diseases. Numerous large-scale neuroimaging projects are enriched with extended exposomic data including sociodemographic, biomedical, lifestyle and environmental measurements. Yet, deciphering how this complex web of influences collectively and dynamically shapes individual brain-behaviour phenotypes comes with substantial challenges. In this Perspective, we first emphasize how the exposome concept refers to a set of interrelated and interacting factors and outline how multivariate pattern learning enables us to account for this complexity. We also highlight temporality as a key challenge, as the timing, duration and sequencing of exposures importantly shape their associations with brain-behaviour phenotypes, and we discuss existing approaches to address these dynamics and their limitations. We further underscore the challenge of causal inferences in population datasets, especially given bidirectional exposome-brain relationships. We then refer to traditional statistical design, as well as generative models and causal machine learning, as promising perspectives to approach data entanglement. Finally, we conclude that, to truly benefit global health, the field also needs to address the lack of global diversity in brain-behaviour exposomic research.

Idiopathic intracranial hypertension (IIH) features elevated intracranial pressure (ICP) without a secondary cause. Trigeminal neuralgia (TN), usually attributed to neurovascular conflict (NVC), has been reported only rarely in association with IIH. We reviewed the literature and present three institutional cases to better characterize this association while explicitly acknowledging the limits of case-based causal inference. We systematically searched PubMed, Ovid Medline, Ovid Embase, Scopus, and Web of Science from inception through August 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The core search combined terms for idiopathic intracranial hypertension or pseudotumor cerebri with trigeminal neuralgia, trigeminal neuropathy, and facial pain. Because causal attribution from case reports is limited, we applied a qualitative framework based on temporality, objective evidence of raised ICP, exclusion of more convincing alternative causes, especially definite NVC, and response to ICP-lowering therapy. Methodological quality was appraised using the Joanna Briggs Institute case report checklist. Quantitative summaries were restricted to published TN phenotypes; trigeminal neuropathy without pain and persistent idiopathic facial pain were summarized separately. Out of 1302 records, 12 case reports met the criteria. Nine described TN, two involved trigeminal neuropathy without pain, and one involved a persistent idiopathic facial pain phenotype. Across the nine TN cases, all patients were female (mean age 38.7 ± 12.5 years). Reported opening pressure ranged from 220 to 480 mmH2O (mean 393.0 ± 89.2 mmH2O), although diagnostic certainty varied, and not all cases fulfilled the full Friedman criteria for definite IIH. Maxillary (V2) and mandibular (V3) involvement predominated, and multidivisional pain was common. Imaging features of raised ICP and improvement after ICP-lowering therapy were recurrent themes, but standardized high-resolution trigeminal magnetic resonance imaging (MRI) sequences were inconsistently reported, limiting exclusion of coexisting classical TN. We additionally describe three institutional cases: two with documented elevated opening pressure (290 and 320 mmH2O) and one probable ICP-related case with suggestive imaging but unavailable opening pressure. In the institutional cohort, dedicated Constructive Interference in Steady State (CISS) or Fast Imaging Employing Steady-state Acquisition (FIESTA) assessment was not uniformly available; in one case, venous contact was of uncertain significance, and in another, microvascular decompression was performed without operative confirmation of arterial compression or pain relief. TN associated with IIH is a rarely reported association. Given the limited case-based evidence, the available data should be regarded as hypothesis-generating rather than definitively causal. When objective intracranial hypertension is present, careful evaluation of both ICP-related markers and alternative TN mechanisms, including NVC, is essential. ICP-lowering therapy has been reported to improve pain in some cases and may address a potential contributing mechanism.

Irregular meal frequency and depressive symptoms: Moderating roles of dietary diversity and breakfast skipping.

Longitudinal associations between dorsal carpal and third metacarpal bone morphology in Thoroughbred foals during growth.

Single-cell and spatial transcriptomic profiling of POU5F1 in Lung Adenocarcinoma: Dynamics, spatial niche, and prognosis via multi-algorithm ML.