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PubMed2026

Diagnostic criteria and severity assessment for syndesmosis injury using magnetic resonance imaging: A systematic review.

High ankle sprains involving syndesmosis injury present challenges in both diagnosis and severity assessment. Magnetic resonance imaging is widely regarded as the preferred modality for evaluating syndesmosis injury and related structural damage. This systematic review primarily examined the diagnostic utility of magnetic resonance imaging. Secondarily, it explores grading and prognostics of syndesmosis injuries with magnetic resonance imaging and identified possible imaging parameters predictive of injury severity. A comprehensive search of MEDLINE, Embase, CINAHL Complete, and Scopus was performed through February 12, 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Peer-reviewed human studies in English that used magnetic resonance imaging to assess syndesmosis injury were included. Excluded were review articles, case reports, abstract-only studies, and biomechanical or cadaveric investigations. Twenty-seven studies comprising 1931 ankles met inclusion criteria. Magnetic resonance imaging demonstrated high diagnostic accuracy for complete tears of the anterior and posterior inferior tibiofibular ligaments. Ancillary signs such as the ring-of-fire edema pattern, distal tibiofibular joint effusion, and widening of the distal joint space exhibited high specificity with variable sensitivity and may assist in grading injury severity. Magnetic resonance imaging in chronic syndesmosis injury primarily detects fibrotic scarring and post-injury changes. Evidence gaps remain regarding the parameters that best determine injury severity and indicate early surgical intervention in competitive athletes. Consolidating multiple magnetic resonance imaging findings into standardized diagnostic criteria may improve reliability and clinical decision-making.

Humans↗
PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗
PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗
PubMed2026

Meniscal preservation in the age of biologics: toward a quantitative decision algorithm for personalized repair.

BACKGROUND: Despite advances in arthroscopic repair and biologic augmentation, surgical indication for meniscal tears remains heterogeneous. No standardized framework currently integrates biomechanical, clinical, and biological determinants to guide repair versus resection. PURPOSE: To develop a quantitative decision model-the Meniscal Preservation Score (MPS)-that unifies biomechanical and biological evidence to stratify reparability potential and standardize treatment selection in meniscal surgery. METHODS: A systematic evidence synthesis conducted in accordance with PRISMA 2020 reporting standards of studies published from 2000 to 2025 in PubMed, Embase, and Scopus identified key determinants of meniscal healing. Five consistent predictors-patient age, vascularity, tear morphology, associated pathology, and activity profile-were weighted through a two-round modified Delphi consensus among ten experienced knee surgeons. The resulting 0-9-point MPS was incorporated into a stepwise decision tree linking lesion morphology, biological context, and surgical strategy. Conceptual validation used 50 simulated cases and a retrospective cohort of 45 patients to test agreement between algorithm recommendations and expert surgical decisions. RESULTS: The MPS achieved 86% concordance with expert judgment in simulation and 84% agreement in clinical validation. In this retrospective exploratory cohort, cases in which surgical management was concordant with MPS recommendations demonstrated higher mean IKDC scores at 24 months and lower observed reoperation rates. These findings should be interpreted as associative rather than causal, as treatment allocation was not controlled and discordant cases may have represented inherently more complex pathology. CONCLUSION: The MPS represents an evidence-informed decision-support framework designed to systematize reparability assessment. While exploratory analyses suggest structural coherence with expert reasoning, prospective implementation and external validation are required before clinical adoption as a predictive tool. LEVEL OF EVIDENCE: conceptual model with exploratory validation.

Humans↗
PubMed2026

An AI-assisted Clinical Decision Support System for Green Classification of Cystocele on Dynamic Transperineal Ultrasound.

Green classification of cystocele on dynamic transperineal ultrasound (TPUS) remains operator-dependent because it requires manual frame selection and landmark-based assessment of the Valsalva maneuver. We developed a workflow-oriented AI-assisted clinical decision support system for automated urethrovesical junction localization and dynamic Green classification and prospectively evaluated its standalone and reader-support performance. This diagnostic accuracy and reader study included 881 patients from a tertiary referral hospital, comprising a retrospective development cohort (n = 688) and an independent prospective test cohort (n = 193). A nested subset of 67 prospective patients was used for a reader study involving two junior and two intermediate radiologists under unaided and AI-assisted conditions. In the complete prospective test cohort, Green-AttGRU achieved a macro-averaged AUC of 0.939 (95% CI, 0.897-0.971) and an overall accuracy of 0.902 (95% CI, 0.860-0.943). In the reader study, overall accuracy increased from 0.761 to 0.821 without AI to 0.851-0.881 with AI, while macro-F1 increased from 0.660 to 0.777 to 0.820-0.860. Overall inter-reader agreement increased from a Fleiss' κ of 0.453 to 0.786, and pooled median interpretation time decreased from 26.7 s to 9.9 s. These findings support the preliminary feasibility of the system as a workflow-oriented decision-support tool for dynamic TPUS interpretation.

Humans↗
PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗
PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗
PubMed2026

The future of pediatric vesicoureteral reflux management.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux (VUR) is a common condition in pediatric urology, yet important uncertainties persist regarding risk stratification, imaging strategies, and prevention of long-term renal damage. Emerging technologies may help address these challenges. This review provides a forward-looking overview of recent advances in artificial intelligence (AI) and immunomodulation that may influence future management of pediatric VUR. METHODS: A forward-looking literature review was performed using the PubMed database (January 2000-March 2025), focusing on studies addressing AI, immunomodulation, or vaccination in the context of VUR and urinary tract infections. Criteria of inclusion were the relevance to pediatric VUR, the novelty of the proposed concept, the potential clinical implications and, for the AI literature, the existence of a clinical evaluation of the algorithm on a dataset from patients. KEY FINDINGS AND LIMITATIONS: AI-based models show promising performance in supporting clinical decision-making, including prediction of the need for voiding cystourethrography, automated grading of VUR, estimation of recurrent urinary tract infection risk and prediction of chemoprophylaxis. These tools may facilitate more individualized diagnostic and therapeutic strategies, although current evidence is largely retrospective and requires prospective validation. Immunization and immunomodulatory approaches aim to reduce infection burden and modulate inflammatory pathways associated with renal scarring. While early experimental and adult clinical data are encouraging, pediatric-specific evidence remains limited, and clinical applicability in children with VUR is not yet established. CONCLUSION: Artificial intelligence and immunologically targeted strategies represent complementary, emerging approaches that may contribute to more personalized management of pediatric VUR. At present, both should be regarded as exploratory tools whose clinical impact will depend on further validation and appropriately designed pediatric studies.

Humans↗
PubMed2026

Vaccine preferences and their role for vaccine confidence and uptake: a meta-ethnography.

Vaccine confidence and uptake are influenced by individuals' preferences regarding vaccine composition, quality, or administration pathways. However, literature synthesizing available qualitative insights into individuals' vaccine preferences remains limited. We therefore conducted a meta-ethnographic systematic review of the qualitative literature on vaccine preferences to identify opportunities for enhancing vaccine confidence and uptake. We implemented a comprehensive search strategy and screened 5,528 studies across seven research databases published between 2001 and 2023. We identified and synthesized 97 qualitative articles to delineate factors influencing consumers' vaccine preferences. Our findings revealed four primary domains shaping individuals' vaccine preferences: Product, Place, Price, and Promotion. First, individuals' preferences for vaccines often hinge on perceived quality and safety of the product itself, which can, for example, be associated with vaccine brand or origin, especially in the case of novel vaccines. Second, people prioritize convenience in terms of vaccination sites and delivery methods (wanting vaccinations offered at their doorstep or in local peripheral clinics); evidence regarding preferred groups to administer the vaccines was mixed. Third, the price of vaccines and the secondary costs associated with vaccination played a role in uptake considerations. Finally, both the sources of information (such as healthcare workers, community volunteers, and religious authorities) and the methods of promoting vaccine information (including face-to-face consultations during clinic visits and the distribution of leaflets or banners), emerged as crucial factors shaping decision-making processes. Overall findings highlight the importance of addressing multifaceted preferences to enhance vaccine confidence and uptake. By understanding individuals' vaccine preferences, strategic recommendations can be developed to optimize vaccination programs and ensure acceptability and utilization.

Humans↗
PubMed2026

Financial incentives and social messaging for repeat SARS-CoV-2 antibody testing among the underserved: A randomized trial.

Financial incentives may influence health behavior beyond their expected monetary value, and their effectiveness may depend on how the behavior is framed. Behavioral theories of decision making suggest that individuals may value protection against small-stakes losses more than expected utility predicts, while theories of family-centered health behavior suggest that messages emphasizing benefits to family members may strengthen participation in preventive health activities. We tested these ideas in a 2×2 factorial randomized trial involving 625 households recruited from a Federally Qualified Health Center serving low-income Latino/Hispanic communities. Participants completed repeat SARS-CoV-2 antibody testing. The trial crossed two messaging strategies (Family vs. Personal) with two incentive structures (Loss Protection vs. Lottery) that offered equivalent expected monetary value. Family Messaging emphasized protecting one's family from COVID-19, whereas Personal Messaging emphasized protecting oneself. Loss Protection allowed participants to secure an at-risk reward through repeat testing, whereas the Lottery condition offered a chance of a large reward. Repeat testing was approximately 8 percentage points higher under Family Messaging and 7 percentage points higher under Loss Protection. Baseline trust in medical providers, financial barriers to vaccination, and risk aversion were associated with initial testing, whereas household characteristics were not associated with repeat testing. Incentive design may matter beyond expected monetary value and that framing health behaviors in terms of family welfare may increase participation in repeated healthy activities. Broadly, the results support behavioral theories emphasizing loss aversion, anticipated regret, and family-centered motivations, and suggest practical approaches for improving engagement in repeat health behaviors. CLINICALTRIALS.GOV REGISTRATION NUMBER:: NCT01901624.

Adult↗
PubMed2026

An overview of the use of proteomics and peptidomics to characterize alternative protein foods.

The global protein transition is accelerating the development of alternative protein foods, mainly derived from plants, insects, algae, fungi, and cellular agriculture. Ensuring the authenticity, safety, and nutritional adequacy of these emerging protein matrices requires molecular-level characterization beyond traditional compositional analyses. Proteomics and peptidomics have emerged as transformative analytical platforms capable of decoding the molecular signatures that define protein origin, structural integrity, digestibility, functionality, and health potential. The review comprehensively examines the application of proteomics, and peptidomics for profiling alternative protein foods. Further, the source authentication strategies based on species-specific protein and peptide biomarkers, detection of adulteration in complex matrices, and allergenicity assessment is discussed. Special attention is also given to nutritional proteomics with protein digestibility, gastrointestinal peptide release, and identification of bioactive sequences. SIGNIFICANCE: The importance of this review is that proteomics and peptidomics are becoming central in the management of the fast-growing environment of alternative protein foods, such as plant-based, insect, algal, fungal, and cultured meat products. It provides an explanation of the application of mass spectrometry-based processes to decode molecular signatures defining the origin of proteins, their structural integrity, digestibility, allergenicity, and bioactive properties, and thus directly contribute to safety, nutritional analysis, and authenticity of the product. Presentation of the article includes the integration of the knowledge of traditional muscle foods with alternative systems of proteins, where validated protein and peptide biomarkers are used in authentication, fraud detection, and allergy risk assessment in a wide variety of matrices. It also indicates the role of nutritional proteomics and peptidomics in informing the formulation strategy to promote digestibility and release of health-promoting peptides. In general, this review will guide scientists, the food industry, and regulatory bodies to use modern proteomic technologies in quality assurance, and decision-making, for the advancementof sustainable protein-based foods.

Proteomics↗
PubMed2026

Making waves: toward systems-level interpretation of hormonal and endogenous biomarkers in wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has proven invaluable for population health monitoring, most notably during the COVID-19 pandemic. Yet current WBE largely relies on exogenous markers such as drugs, pathogens, and their metabolites, limiting surveillance to what communities are exposed to. We argue for expanding WBE towards endogenous biomarkers, particularly hormones, which provide insights into physiological stress, metabolic function, and endocrine activity. Hormone-based WBE offers new opportunities to capture population-level biological responses to societal and environmental stressors, disasters, and chronic disease burdens at the community scale. This perspective outlines a systems-level framework for integrating hormonal signals in wastewater with clinical data, behavioral indicators, environmental factors, and digital markers to support more robust and context-aware public health surveillance. We highlight key technical considerations, interpretive challenges, and opportunities for translational pilot studies. By moving beyond exposure tracking toward more integrated interpretation of biological responses, hormone-informed WBE may contribute to more resilient, inclusive, and actionable public health infrastructure.

Humans↗
PubMed2026

Test-retest reliability of spatiotemporal, kinematic, and kinetic measures in marker-based 3D gait analysis: A systematic review.

BACKGROUND: Marker-based 3D gait analysis (3DGA) is widely used to quantify impairments and evaluate treatment effects. For longitudinal clinical interpretation, clinicians and researchers need reference values for inter-session measurement error. For this purpose, this systematic review synthesized Standard Error of Measurement (SEM) values for spatiotemporal, kinematic, and kinetic (moments) outcomes obtained from marker-based 3DGA studies. METHODS: PubMed and Scopus were searched (final search: 11 December 2025). Studies reporting inter-session test-retest SEM and/or MDC for steady-state overground or treadmill walking using marker-based motion capture were included. Two authors screened records and appraised methodological/reporting quality using a custom tool informed by COSMIN, GRRAS, and biomechanics-specific items. Due to heterogeneity, results were synthesized descriptively using study-level median SEM values, stratified by joint, plane, population (healthy, pathological, single subgroups), and walking condition. Minimal Detectable Change (MDC) values were computed for all available data. RESULTS: Thirty-four studies (762 participants, 44.2% females) were included, with substantially more evidence for overground than treadmill walking. Overground spatiotemporal outcomes showed low errors (walking speed SEM of 0.06 m/s; timing typically ≤0.03 s; spatial parameters generally ≤0.03 m). For joint kinematics during overground walking, median SEMs were 2.4° (sagittal), 1.9° (frontal), and 3.3° (transverse). The corresponding joint-kinetic SEMs were approximately 0.06, 0.04, and 0.03 Nm/kg, respectively. Treadmill data followed similar patterns. SIGNIFICANCE: Marker-based 3DGA allows for accurate assessment of spatiotemporal, kinematic, and kinetic gait features. We provided detailed SEM/MDC lookup tables to support clinical decision-making. Results further offer a benchmark for validating emerging gait assessment technologies (e.g., markerless systems) against realistic limits of marker-based 3DGA.

Humans↗
PubMed2026

Utility of Dynamic MRI in Surgical Outcome of Patients With Degenerative Cervical Myelopathy: A Single-Center, Randomized Controlled Trial.

BACKGROUND AND OBJECTIVES: The utility of dynamic MRI (dMRI) in surgical planning and outcomes for degenerative cervical myelopathy (DCM) has not been validated in any prospective randomized trials. METHODS: In this hospital-based randomized controlled trial conducted between February 2023 and December 2024, patients with DCM were randomized into 2 groups: the Static MRI Group, where surgery was guided by conventional static MRI alone, and the dMRI Group, in which dMRI was performed, with the potential to alter the surgical approach. The primary outcome was recovery rate (RR) at 3 months. Secondary outcomes included postoperative changes in modified Japanese Orthopaedic Association scores and Nurick grades, surgical plan alterations, comparison of surgical approaches, and complication rates. RESULTS: Seventy-four patients were analyzed at a 3-month follow-up. The dMRI group had a significantly higher mean RR (55.42% ± 29.05%) than the Static group (46.76% ± 29.51%) ( P = .044). A RR of ≥50% was observed in 91.9% of patients in the dMRI group, compared with 59.4% in the static MRI group ( P = .002). Modified Japanese Orthopaedic Association scores improved more in the dMRI group (15.47 ± 2.62 vs 13.77 ± 2.66, P = .007). While Nurick grades improved in both groups, the intergroup difference was not statistically significant ( P = .151). dMRI altered the surgical plan in 59.5% of cases. Anterior approaches yielded better RR but had more complications. By contrast, posterior approaches had fewer but more severe complications including mortality. CONCLUSION: dMRI enhances the detection of clinically significant cord compression and may aid in surgical decision-making, potentially contributing to superior functional outcomes in DCM. Further studies are required to determine its impact on long-term functional outcomes.

Humans↗
PubMed2026

Alarms and alarm management with automated versus conventional ventilation in neurocritical care patients.

INTRODUCTION: False or clinically irrelevant alarms are a major driver of ICU alarm fatigue and nursing workload. Ventilator alarms make up a large share, and although automated ventilation modes can reduce manual adjustments, their effect on alarm burden is still unclear. This issue can be particularly relevant in neurocritical care patients, where precise ventilator and alarm management is imperative for patient safety. OBJECTIVES: This explorative post hoc analysis of a randomized clinical trial compared alarm frequency and management between automated ventilation and conventional ventilation in neurocritical care patients. METHODS: Ventilator alarms and manual ventilator changes were captured continuously from the ventilator for up to 24 h per patient. The primary endpoint was a composite of workload-relevant alarms; with alarm management interventions at the ventilator as a key secondary outcome. Additional endpoints included redundant alarms, alarm duration and ventilator management. RESULTS: 13 patients received automated ventilation and 24 received conventional ventilation. No difference was observed in workload-relevant alarm frequency between automated and conventional ventilation (3.28 [2.87 to 4.30] vs 3.73 [1.66 to 7.33] alarms per hour; P = 0.81), while alarm management interventions at the ventilator were lower with automated ventilation (0.14 [0.10 to 0.15] vs 0.21 [0.17 to 0.31] interventions per hour; P = 0.01). Other alarm frequencies, duration of alarms and ventilator management were similar. CONCLUSIONS: In this exploratory post hoc analysis of a randomized clinical trial in neurocritical care patients during the early phase of mechanical ventilation, automated ventilation did not reduce the frequency of total or workload-relevant alarms, nor their duration, but was associated with fewer alarm management interventions compared to conventional ventilation. IMPLICATIONS FOR CLINICAL PRACTICE: Automated ventilation may not reduce alarm frequency in neurocritical care patients, but the observed reduction in alarm-related bedside interventions suggests a potential benefit for nursing workload.

Humans↗
PubMed2026

CanDo (Canadian Donor Milk) randomised controlled trial: pasteurised human donor milk supplementation in the well-baby unit - protocol.

INTRODUCTION: Mother's milk is the gold standard for feeding newborns. Despite lactation support while in hospital, supplementation rates remain high in Canadian well-baby units at 35-50%. When supplementation is needed, the choice between formula milk and pasteurised human donor milk (donor milk) remains uncertain with a lack of clinical trials to inform this practice. This study aims to compare the effect of supplementing mother's milk with donor milk versus formula in infants at higher risk for supplementation (infants of diabetic mothers, infants born small for gestational age or with a birth weight less than 2.5 kg and late preterm infants born between 350/7 and 366/7 weeks gestation). METHODS AND ANALYSIS: This is an ongoing, open-label, single-centre, randomised controlled trial conducted at Mount Sinai Hospital, Toronto, Canada. A total of 112 infants (56 per group) will be randomised to receive donor milk or infant formula as a supplement to mother's milk during their initial hospital stay, when supplementation is deemed necessary by the family and/or healthcare team. The primary outcome is exclusive human milk feeding at 4 months of age. Secondary outcomes include any or exclusive human milk feeding at 1, 2 and 3 months; infant growth and health indicators and breastfeeding self-efficacy. Exploratory outcomes encompass infant temperament; parental mental health (assessed using the State-Trait Anxiety Inventory and Edinburgh Postnatal Depression Scale); milk cortisol concentrations; and informal milk sharing comparing donor milk and formula supplementation. Follow-up includes monthly telephone assessments and a virtual or in-person visit at 4 months post partum. Data will be analysed using intention-to-treat principles. ETHICS AND DISSEMINATION: The CanDo trial has received ethics approval from the Mount Sinai Hospital Research Ethics Board and the University of Toronto. Results will be disseminated through peer-reviewed journals, conference presentations and stakeholder engagement with hospital and public health decision-makers. Findings will address a critical evidence gap regarding the use of donor milk supplementation in well-baby units and may inform future clinical practice and policy in newborn feeding. TRIAL REGISTRATION NUMBER: NCT06315127.

Humans↗
PubMed2026

Incidence and risk factors for malignancy in patients with incidental solitary pulmonary nodules: a systematic review and meta-analysis.

BACKGROUND: The increasing use of chest imaging has led to a higher detection rate of incidental solitary pulmonary nodules (SPNs), often causing patient anxiety. Determining the malignancy rate and associated risk factors is crucial for developing appropriate follow-up strategies to prevent overdiagnosis, overtreatment, or missed diagnoses. This meta-analysis aims to investigate the malignancy rate and risk factors in patients with incidental SPNs. METHODS: A systematic search of PubMed, Embase, Web of Science, and the Cochrane Library was conducted up to June 30, 2025. Data on malignancy rates and potential risk factors were extracted from eligible studies. All pooled analyses were performed using a random-effects model. RESULTS: Fifty-four studies involving 19,985 patients were included. The pooled malignancy rate for incidental SPNs was 56.7% (95% CI: 51.5-62.0), with significant between-study heterogeneity (I2 = 98.5%, p&#x2009;<&#x2009;0.001). The pooled effect size showed a minimal change after adjustment for potential publication bias using the non-parametric Trim-and-Fill method (54.7%; 95%CI: 50.9-58.8). Risk factor analysis identified that older age, history of cancer, cigarette smoker, larger nodule diameter, spiculation, upper lobe location, lobulation, pleural indentation, vascular convergence, solid nodules, family history of cancer, and irregular or ill-defined margins were significantly associated with an increased risk of malignancy. Conversely, male sex, presence of calcification, and clear borders were significantly associated with a reduced risk of malignancy. CONCLUSION: This meta-analysis provides a comprehensive assessment of malignancy rates and risk factors in incidental SPNs. The high pooled malignancy rate should be interpreted considering the significant heterogeneity and the inclusion of a high proportion of retrospective studies and populations from high-risk regions. Nonetheless, these findings offer essential evidence for clinical risk stratification, supporting optimized follow-up and informed decision-making.

Humans↗
PubMed2026

Genetic overlap between estimated glomerular filtration rate and cardiovascular disease identifies potential targets for cardiorenal syndrome.

Heart and kidney diseases frequently coexist, but the genetic basis of this relationship remains unclear. We analyzed genetic data from large-scale studies to investigate how kidney function (estimated glomerular filtration rate, eGFR) and six common cardiovascular diseases share genetic risk factors. Using MiXeR method, and conjunctional false discovery rate (conjFDR) to identify overlapping genetic regions, we found 478 shared genomic loci between eGFR and cardiovascular diseases. These shared genes are involved in tissue development and structure. We also identified 29 genes that could be targeted by existing medications approved by the US Food and Drug Administration, such as PRKAG2, PDE1A, and IGF1R. Among these, genetically predicted higher level of IGF1R expression is associated with a higher eGFR, which reflects good kidney function and is protective against cardiorenal diseases, such as atrial fibrillation, and myocardial infarction. These findings reveal genetic overlap between kidney function and cardiovascular diseases, highlighting potential targets for understanding and treating cardiorenal syndrome.

Humans↗