Background/Objectives: Real-world evidence on dupilumab-induced clinical remission on treatment and on the predictors of complete response in severe uncontrolled asthma (SUA) is still limited. Methods: We conducted a single-centre ambispective uncontrolled observational study including 152 adults with SUA who started dupilumab. Lung function, type-2 (T2) biomarkers, patient-reported outcomes, exacerbations, oral corticosteroid (OCS) use, emergency-department (ED) visits and hospitalisations were compared between baseline and last follow-up. Response was graded with FEOS and EXACTO. Independent predictors of complete response (EXACTO = 1) were explored by multivariable logistic regression. Results: Mean age was 50 ± 12 years, 67.1% were women, baseline FEV1 was 82 ± 22% predicted, blood eosinophils were 612 ± 577 cells/µL and FeNO was 55 ± 46 ppb. After 21 ± 8 months, ACT improved from 15.7 to 23.3, ACQ-5 from 4.0 to 0.35, mini-AQLQ from 2.18 to 4.53, SNOT-22 from 52 to 16, and FEV1 by 275 ± 180 mL (all p < 0.001). Annual exacerbations decreased from 7.9 to 0.7, ED visits from 3.17 to 0.28, hospitalisations from 0.71 to 0.08, and maintenance OCS use from 11.8% to 3.9%. According to EXACTO, 95 patients (62.5%) achieved complete response/super-response. Independent predictors were higher baseline FEV1, higher eosinophil count, allergic rhinitis, rhinosinusitis with nasal polyps and atopic dermatitis, whereas prior OCS bursts and higher FeNO were inversely associated. Model discrimination was adequate (AUC = 0.91). Conclusions: Dupilumab achieved clinical remission on treatment in almost two-thirds of patients with SUA and markedly reduced healthcare utilisation. A T2-comorbidity-rich profile identified patients with the highest probability of complete response.

Background: The rapid integration of Artificial Intelligence (AI), specifically Clinical Decision Support Systems (CDSS), into healthcare offers substantial efficiency but introduces critical ethical and legal challenges, particularly the perpetuation of systemic bias against older adults ("Digital Ageism"). While technological advances may improve care, they can violate fundamental bioethical principles when models are trained on unrepresentative data. Aim: This article argues that traditional clinical risk-management models are structurally insufficient to address opaque algorithmic bias and presents a conceptual, multidimensional governance framework designed to prevent the codification of human ageism into AI infrastructure. Methods: Drawing on systemic failures observed during the COVID-19 pandemic, the normative model integrates legal and governance standards aligned with the EU AI Act, Explainable AI (XAI) tools, and a three-phase implementation protocol. Results: To illustrate potential application without overburdening medical staff, the article introduces a theoretical Targeted Escalation Protocol and an Autonomous High-Load Safety Mode. The latter applies deterministic hardcoded constraints to contain age-dominant outputs during acute surges while preserving attending-clinician authority. The framework is explored through an Intensive Care Unit (ICU) thought experiment. Conclusions: The framework provides a structured roadmap for policymakers, ethicists, and healthcare administrators to move from reactive defensive medicine toward proactive ethical safety, safeguarding the dignity of the aging population while aiming to mitigate institutional legal exposure.

Balance is reliant on key sensory information to maintain postural control. Proprioception is one of the key sensory inputs that influence postural control during static balance tasks. Poor proprioception can lead to impaired balance, which may be amplified under dual-task balance conditions. This study investigates this relationship between proprioception and dual-task balance performance and how it may be affected by age. Proprioception was measured for each ankle using Active Movement Extent Discrimination Assessment (AMEDA), a protocol which determines ankle kinaesthesia acuity through active, weight-bearing ankle inversion movements. Participants completed 12 static dual-task balance conditions (feet together, tandem stance) paired with a cognitive task (control task, digit span forward, and backward). Balance performance was measured by centre of pressure trajectories. Relationships between ankle kinaesthesia and dual-task balance were analysed with linear mixed effects models accounting for age, balance condition and cognitive task. Thirty participants (mean age 52.4, SD 16.4) were recruited. Mixed effects models showed significantly greater sway with tandem foot position (compared to feet together, p < .001) in all models. Higher AMEDA score was associated with lower body sway area (p = .025). AMEDA score was not related to mediolateral or anteroposterior sway range. Older age was associated with greater maximum sway range in the mediolateral (p = .019) and anteroposterior directions (p = .032) and approached significance for body sway area (p = .058). Superior mean ankle kinaesthesia is associated with less body sway area under dual-task conditions, while older age is associated with a greater sway range. Findings suggest proprioception influences dual-task balance performance, such that the ability to control body sway is greater when ankle kinaesthesia is greater. Future studies should consider incorporating single leg stance under dual-task conditions to investigate if the influence of proprioception varies for individual ankle kinaesthesia and limb dominance.

Sport-related mild traumatic brain injury (mTBI) or concussion is common and has long-term implications. The lack of diagnostically accurate, rapid, and easy-to-administer tests exacerbates the problem. We evaluated the objectiveFIELD Analyser® (OFA®) for mTBI. This cross-sectional study included athletes who had sustained a concussion, and two groups of controls: the putative control group (pCG) comprising rugby players who claimed they had never had a concussion, and a non-rugby normal control group (nCG). Two OFA tests, the 8 min (OFA30) and the rapid 90 s (OFA30-12), were performed. Discrimination was performed against both the control groups using the Area Under Receiver Operating Curves (AUROC) and Hedge's g standardised effect size. The athletes were divided into the Acute Group, with 42 athletes tested within 15.4 ± 13.6 days of mTBI, and the Chronic Group, with 23 athletes tested within 941.5 ± 769.0 days. Subjects were age-matched (22.4 ± 3.06 years). For the nCG, OFA30-12 performed better than OFA30: with Hedge's g values of 1.22 in acute and 1.45 in chronic cases, compared with 0.93 for acute and 1.00 for chronic cases. AUROCs performed similarly. Notably, when compared with the pCG, both tests showed poorer diagnostic power. OFA perimetry showed potential as reliable, rapid test for assessing concussion. The results cannot be generalized to the first 72 h following a concussion due to insufficient data from participants assessed within this early period.

Although diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, existing prognostic tools, including the revised International Prognostic Index (R-IPI) and the National Comprehensive Cancer Network (NCCN-IPI), do not fully capture the heterogeneity and their performance in the competing-risks framework has not been validated. We developed and validated prognostic models for overall survival (OS) and progression-free survival (PFS) using both machine-learning (ML) and standard regression approaches in 2769 patients from the Lymphoma and Related Diseases Registry. Predictors included age, stage, performance status, chemo-immunotherapy, creatinine, lactate dehydrogenase, and anaemia, with extranodal involvement additionally used for OS and BCL6 expression for PFS. Both ML and regression-based models showed similar performance, with acceptable discrimination, particularly at 1- and 2-years. In validation, the Cox model achieved a 1-year OS AUC of 0.770, outperforming R-IPI (0.722) and comparable to NCCN-IPI (0.746). Both the nomogram and random survival forest models demonstrated greater 5-year OS risk separation, ranging from 26% in high-risk to 96% in low-risk patients and from 25 to 93%, respectively, compared with the R-IPI (50-91%) and NCCN-IPI (34-96%). Competing-risks analyses demonstrated that conventional methods underestimated survival, particularly in high-risk groups. While our models provided promising risk stratification, external validation is warranted.

Over the years, the application of ultrasound as a non-invasive diagnostic tool has become increasingly widespread in clinical practice. Additionally, the incidence of hypoxemia is relatively high among patients undergoing painless gastroscopy. The primary objective of the present study was to predict the occurrence of hypoxemia in painless gastroscopy by measuring the anatomical parameters of the upper respiratory tract using ultrasound. The present single-center, prospective, observational study included 218 patients. Before anesthesia, basic information (age, sex, BMI) was collected. Preanesthetic ultrasound examined upper airway parameters, including distance from the skin to the hyoid bone (DSH), distance from the skin to the epiglottis (DSE), distance from the skin to the anterior commissure of the vocal cords (DSAC), tongue thickness (TT), hyoid-mental distance (HMD) and anterior condylar translation (ACT). Binary logistic regression was applied to identify independent predictors, and the predictive performance of each variable and a combined model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration analysis and decision curve analysis (DCA). A total of 218 patients were analyzed in the present study. A binary logistic regression model identified BMI, DSH, DSE, DSAC, TT and ACT as the independent indicators associated with hypoxemia in painless gastroscopy. A novel combined predictive model equation was derived: logit(P)=1.053 x BMI + 1.363 x DSH + 1.658 x DSE + 1.839 x DSAC + 1.075 x TT-0.894 x ACT. The model demonstrated excellent discrimination with an AUC of 0.989, 95% CI: 0.979-0.999, significantly outperforming individual predictors (AUC range: 0.775-0.879). Calibration curves confirmed good agreement between predicted and observed risk, and DCA indicated clinical utility of the model. In conclusion, BMI, DSH, DSE, DSAC, TT, ACT and the novel combined predictive model incorporating aforementioned parameters are potentially valuable predictors fort the occurrence of hypoxemia in painless gastroscopy. This study was registered at the Chinese Clinical Trial Registry (trial registration no. ChiCTR2500109627; registered on 23 September 2025).

In-hospital outcomes after acute myocardial infarction (AMI) treated with percutaneous coronary intervention remain variable. Documentation-derived nursing process indicators may act as proxy markers of clinical acuity, surveillance intensity, and care complexity, but their association with short-term outcomes remains unclear. This single-center retrospective cohort study included consecutive adults with AMI. Nursing process indicators were extracted from routine nursing records, including assessment frequency, monitoring intensity, documentation density, and selected observational items. The primary endpoint was a composite of all-cause in-hospital death, recurrent myocardial infarction, malignant arrhythmia, cardiogenic shock, major bleeding, or prolonged hospitalization. Multivariable logistic regression was used to estimate adjusted odds ratios (ORs). Intensive care unit admission was examined as a contextual care-setting variable but was not included in the final extended discrimination model. Internal model discrimination was assessed using receiver operating characteristic analysis and area under the curve comparison by DeLong test; no external validation was performed. Of 512 screened patients, 438 were included in the final analysis; the mean age was 61.4 years, and 73.5% were male. During hospitalization, 168 patients (38.4%) experienced the composite primary endpoint; all-cause in-hospital mortality occurred in 4.1%, and major adverse cardiovascular events occurred in 14.2%. After multivariable adjustment, higher nursing documentation density was associated with the composite endpoint (per additional record per day: adjusted OR: 1.29, 95% confidence interval [CI]: 1.08-1.55; P = .005), as was documented cardiac rhythm monitoring (adjusted OR: 1.88, 95% CI: 1.25-2.84; P = .002). In an internal discrimination analysis without external validation, the baseline clinical model showed an AUC of 0.71 (95% CI: 0.66-0.76), whereas the extended model including selected documentation-derived nursing process indicators showed an AUC of 0.76 (95% CI: 0.71-0.81; ΔAUC = 0.05; DeLong P = .012). In this real-world cohort of patients with AMI undergoing percutaneous coronary intervention, documentation-derived nursing process indicators were associated with in-hospital adverse outcomes and provided modest incremental internal discriminative information beyond traditional clinical predictors.