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PubMed2026

Access Block and Ambulance Ramping: The Canaries of the Healthcare System.

OBJECTIVE: To identify evidence-based factors leading to the global challenge of hospital access block and inform strategies to improve emergency access performance. METHODS: A mixed methods approach was followed comprising an umbrella review of published systematic reviews, qualitative analysis of the perspectives of patients and healthcare workers, and quantitative analysis of contextual factors and 6 years of ambulance, emergency inpatient and ward movement records for the 25 largest public hospitals in Queensland, Australia. RESULTS: A key set of findings and recommendations were identified to improve emergency access that are practical and actionable. These comprise the introduction of inpatient discharge metrics and monitoring to shift focus from the front door of hospitals to the 'back door'; increasing support for primary care, community care, aged care, NDIS and vulnerable groups; maintaining demand-side strategies such as increasing inpatient-equivalent care alternatives (e.g., hospital in the home, acute care within nursing home services); investment in prehospital flow; improving hospital processes such as extended-hour discharge lounges; improving workforce; and revising funding policies. CONCLUSIONS: The study findings fill a gap in the evidence regarding challenges and recommendations for improving patient flow within hospital emergency departments and across the broader health system. Focussing efforts at the 'back end' of the inpatient journey is a critical step to improve emergency care outcomes.

Humans↗

PubMed2026

Effectiveness of Embedded Social Media Content on E-cigarette Attitudes and Behaviors: Results from a Randomized Control Trial.

BACKGROUND: This longitudinal randomized controlled trial examined the effects of anti-vaping social media content on e-cigarette attitudes and intentions among U.S. young adults (ages 18-24; n=3,400). METHODS: Targeted content was embedded directly into participants' social media feeds, with varying levels of impressions. RESULTS: Results indicate that increased exposure to anti-vaping messages significantly elevated perceived risk of harm (β=0.11, 95% CI: 0.03-0.20, p < .01) and social unacceptability of e-cigarette use (β=0.10, 95% CI: 0.03-0.18, p < .01), while decreasing intentions to vape (RRR = 0.59, 95% CI: 0.36-0.97, p < .05). Notably, these attitudinal shifts occurred even with relatively low ad exposure and over an extended intervention period, while controlling for the e-cigarette use status (never, former, or current) of participants. Discussion These findings support the effectiveness of digital media campaigns in influencing health-related behaviors and attitudes among young adults. Specifically, embedding anti e-cigarette content directly in the feed of young adults is shown to be effective in shifting attitudes in a space where these young adults are already engaged. Limitations include limited ad impressions and minimal change in ad awareness recall, suggesting future research should explore longer interventions and broader nicotine product messaging.

Humans↗

PubMed2026

Weight Loss without Food Intake Suppression through Size-Dependent Retention of Anti-Inflammatory Nanomedicines.

Obesity is a risk factor for high-mortality health conditions, including cardiovascular diseases and type 2 diabetes, which makes the advancement of efficacious and safe weight loss therapies a high priority in pharmacology. The causal link between obesity and its comorbid conditions is believed to be a chronic state of inflammation originating within adipose tissue, with macrophages playing central roles, an axis that is not targeted directly by current therapies. Here, we use nanocarriers to deliver an anti-inflammatory glucocorticoid receptor agonist to adipose tissue macrophages and report the impact of size on therapeutic effect. Three dextran nanocarriers between 4-30 nm in hydrodynamic diameter released molecular drug cargo at equivalent rates and exhibited similar biological potency in vitro. In vivo in a mouse model of obesity, body weight and body fat were reduced in a size-dependent manner after 2-4 weeks of treatment. Unlike current clinical pharmacotherapies for weight loss, these body composition changes were not associated with changes in food intake. Greater retention of larger dextran nanocarriers in visceral adipose tissue appears to elicit a local change to promote browning by increasing mitochondrial abundance and lipid droplet fragmentation. Further development of this platform may result in a safe and potent modulator of adipose tissue in the state of obesity without direct action on nutrient intake to address malnutrition and lean body mass deficiencies observed with current weight loss pharmacotherapies.

Animals↗

PubMed2026

Integration of ear and hearing care services in low- and middle-income health systems: a systematic review and qualitative synthesis.

Hearing loss is a global public health burden and mostly affects those living in low- and middle-income countries (LMICs). One approach to address ongoing challenges is the World Health Organization's recommendation for the integration of ear and hearing care (EHC) services into healthcare packages. However, little is known about EHC integration approaches, particularly in LMICs additionally, these approaches have not been investigated through a health systems lens. This qualitative review aimed to describe the various approaches to the EHC service integration in LMICs and to identify enabling and constraining factors. We reviewed 17 studies, with a focus on LMICs, using adaptations of the Valentijn integration and World Health Organization EHC frameworks, following the PRISMA guidelines. Our investigation showed that most integration approaches were at micro or individual level. Enabling factors for integration of EHC services were training, mentorship, collaboration, technology, inclusion of EHC in healthcare packages and investment in EHC services. Barriers were challenges with training, facilities and equipment, policy implementation and resourcing of EHC services. We further described factors influencing healthcare seeking behaviour and the use of integrated EHC services, such as access and ability to pay, referral systems and communication and awareness. This study describes the complex nature of EHC integration and ways to support integration. Key considerations are the level of integration, training to address workforce issues and factors influencing service utilisation as we work towards health system strengthening.

Humans↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2026

Metabolic and endocrine modulation of the gut-adipose tissue axis via pro-, pre-, and postbiotics in overweight dogs: A systematic review.

Canine obesity is a complex metabolic disorder driven by luminal dysbiosis, impaired gut barrier function, and metaflammation. Following PRISMA 2020 guidelines, this systematic review evaluated the efficacy of pro-, pre-, and postbiotics in modulating the gut-adipose tissue axis in overweight dogs (BCS ≥ 6/9) or diet-induced obesity models. Searches across PubMed and Dimensions (April 2026) identified seven eligible experimental trials. Results suggest that postbiotic Bifidobacterium animalis subsp. lactis CECT 8145 reduced postprandial glucose AUC by 6 % strictly during energy restriction. Pasteurized Akkermansia muciniphila postbiotics limited diet-induced weight gain, though glucoregulatory impacts were highly strain-specific (AKK2 reduced fasting glucose and insulin resistance indexes, whereas EB-AMDK19 exerted no significant effect). Specific probiotics (including Enterococcus faecium, Bifidobacterium lactis, Lactiplantibacillus plantarum and Bifidobacterium breve) attenuated fasting hyperinsulinemia and preserved circulating adiponectin, but lipid profile improvements (triglycerides and total cholesterol) were inconsistent across trials. In dogs, increased luminal short-chain fatty acids are not consistently mirrored by endocrine responses, so the coupling between microbial metabolites and incretin signaling remains incomplete. A critical lack of standardized reporting for species-validated insulin sensitivity metrics was identified. In conclusion, microbiome-targeted therapies, particularly inanimate postbiotics, may represent useful adjunctive strategies to mitigate metabolic dysregulation in obesogenic environments. However, clinical efficacy remains strictly strain-specific and dependent on host energy balance. Given the scarcity of high-certainty evidence, future trials must integrate dynamic physiological assessments with species-validated surrogate indexes alongside standardized dietary controls.

Animals↗

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

Probiotic-derived extracellular vesicles as food-based nanocarriers: Mechanisms, functional applications, and future perspectives in food systems.

Probiotic-derived extracellular vesicles (PDEVs) are a promising type of postbiotic nanoparticle derived by fermentation of probiotics, and have gained growing interest as a potential application in food science and nutrition. These are lipid bilayer vesicles of nanoscale, which are naturally released by probiotic cells and contain a wide variety of bioactive molecules, such as proteins, nucleic acids, and metabolites. Moreover, PDEVs are highly stable, biocompatible, and can be easily engineered to have surfaces with high functionality, which makes them good candidates in functional engineering. In contrast to traditional live probiotics, PDEVs overcome the difficulties of preserving microbial viability during processing and storage, thus providing superior safety, stability, and predictable biological performance. This is a systematic review of the various functions of PDEVs in food systems. We conclude on the processes through which PDEVs control intestinal barrier integrity, alter gut microbiota composition, and alter host immune responses, and their potential to enhance gut health when added to functional foods. In addition to their health-promoting effects, PDEVs have shown significant potential as natural antimicrobial agents to preserve food and as effective nanocarriers of hydrophobic bioactive compounds, including fucoxanthin, to improve their stability, bioavailability, and targeted delivery. Moreover, PDEVs can be used as new regulators of microbial fermentation. However, it should be noted that a lot of the evidence that is available is still preliminary and the effectiveness of these applications in real food-processing and storage conditions has not been fully proven. Although they have potential, there are a number of challenges that still hinder the widespread use of PDEVs in the food industry. These involve the creation of scalable and cost-effective production processes, batch-to-batch consistency, vesicle stability in a variety of food matrices, and regulatory and safety considerations. Other emerging engineering approaches, such as surface functionalization and cargo loading, are also discussed in this review and could further increase the specificity, functionality, and application versatility of PDEVs in food systems. Moving forward, the incorporation of PDEVs into the next generation functional foods, novel food preservation methods, and customized nutrition plans should be prioritized in future studies. Further developments in these fields can make PDEVs useful platforms at the interface of food microbiology, nanotechnology, and human health.

Probiotics↗

PubMed2026

Simultaneous determination of imiquimod and terbinafine in skin permeation studies: Validation of a liquid chromatography method with fluorescence detection.

Chromoblastomycosis is a chronic, neglected subcutaneous mycosis posing significant therapeutic challenges. A topical strategy combining terbinafine (TBF), an antifungal, with imiquimod (IMQ), a TLR-7/8 agonist immunomodulator, has emerged a promising alternative. However, no validated analytical method is currently available to simultaneously quantify both drugs in skin, which is crucial for novel formulation development. This study reports the development and validation of a simple HPLC method with fluorescence detection (excitation 236 nm, emission 340 nm) for the simultaneous determination of TBF and IMQ extracted from porcine skin. Separation was achieved on a C8 reversed-phase column (125 × 4.0 mm, 5 μm) using a mobile phase of methanol and water (60,40, v/v), both containing 0.1% formic acid at a flow rate of 0.8 mL/min. The method showed excellent linearity (r > 0.999) over 0.01-1.0 μg/mL for IMQ and 0.1-2.0 μg/mL for TBF. Intra- and inter-day precision demonstrated coefficients of variation below 5%, and recovery rates from skin (79-105%) confirmed accuracy. Limits of detection were 0.001 μg/mL for IMQ and 0.004 μg/mL for TBF, with quantification limits of 0.02 μg/mL and 0.16 μg/mL, respectively. This selective, sensitive, and reproducible method represents a valuable analytical tool for supporting the development and quality control of topical formulations for chromoblastomycosis and other fungal skin diseases.

Animals↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Patterns and implications of co-use between vaping and hallucinogens: a systematic review and meta-analysis.

BACKGROUND: The co-occurrence use of e-cigarettes and hallucinogens has become increasingly common, particularly among youth and young adults. However, evidence regarding the association between these behaviors remains limited and fragmented. This systematic review and meta-analysis aimed to synthesize current evidence, examining the correlation between hallucinogen use and the likelihood of being an e-cigarette user. METHODS: A comprehensive search was conducted in PubMed, Scopus, Web of Science, EMBASE, and Cochrane CENTRAL up to June 2025. Eligible studies measured both hallucinogen and e-cigarette use and reported quantitative associations between these behaviors. Data extraction and risk-of-bias assessments were performed independently by three reviewers using the Newcastle-Ottawa Scale. Pooled effect sizes were calculated using a random-effects model (REML). Certainty of evidence was evaluated with the GRADE approach. RESULTS: Eleven studies met the inclusion criteria (n = 247,904), and seven were included in the meta-analysis (n = 217,478). The pooled analysis demonstrated that hallucinogen users had 4.47 times higher odds of being e-cigarette users (OR: 4.47, 95% CI 2.72 to 7.34; p < 0.001; I2 = 95.7%, n = 7). The certainty of evidence was rated as low. CONCLUSIONS: Hallucinogen use is directionally and strongly associated with e-cigarette use across diverse populations. Although the direction of association was consistent across studies, the magnitude of effect was heterogeneous. These behaviors likely share psychosocial and environmental determinants, although alternative explanations, including shared genetic liability, recall bias, and residual confounding, cannot be excluded. Further longitudinal studies are needed to clarify the underlying mechanisms of this association and establish temporality. The findings also support integrating hallucinogen-use screening into e-cigarette prevention and harm-reduction programs targeting youth and young adults.

Humans↗

PubMed2026

Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.

OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.

Humans↗

PubMed2026

E-cigarette product characteristics and packaging features and interest in e-cigarette use: Results from a randomized within-person trial nested in three prospective cohorts.

BACKGROUND: Product characteristics and packaging may be key targets for regulation to reduce e-cigarette use among youth, but existing data are limited. METHODS: Data are from an experimental study (2018-2020) nested within three prospective cohorts (age 14-26) in southern California (N = 3565). Participants were shown five e-cigarette (e-liquid) packages in a random order that varied in flavor (sweet vs. tobacco), flavor name (descriptive ["blueberry cheesecake"] vs. concept ["smurf cake"] vs. none [number only]), and cartoon image on package (yes/no). For each stimuli, survey items assessed the following outcomes: product appeal (self-enjoyment, others' enjoyment; Likert scale [1-5]), susceptibility to use (use if friends offered, curiosity; 4 ordered responses [definitely not-definitely yes]), peer acceptability (definitely not-definitely yes), and perceived harm (definitely not-definitely yes). Mixed effects proportional odds models evaluated within-person effects of each factor (flavor, flavor name, cartoon) with each outcome. RESULTS: Participants reported greater appeal, susceptibility, and peer acceptability (OR range=3.7-16.9; ps<0.05), and lower perceived harm (OR=0.61; 95%CI=0.53, 0.70) for sweet (vs. tobacco-flavored) e-cigarettes; effects were progressively stronger for younger cohorts. The descriptive flavor name rated higher than the concept flavor (OR range=1.13-2.02; ps<0.05) or number only (OR range=1.18-1.69; ps<0.05) for appeal and susceptibility measures; no differences for concept vs. number were found. The cartoon image rated higher for appeal, curiosity, and peer acceptability (OR range=1.24-1.51; ps<0.05). CONCLUSIONS: Sweet flavors, descriptive flavor names, and cartoon images may increase the appeal of e-cigarettes among youth and young adults with no history of e-cigarette use, and are key targets for regulation.

Humans↗

PubMed2026

Diurnal differences in the effects of heat exposure on renal function: A randomized controlled crossover trial.

High temperature is a major risk factor for kidney injury, and population exposure to nighttime heat is increasing as the climate warms. However, whether renal responses to heat exposure differ between daytime and nighttime remains unclear. Forty-one healthy adults participated in a randomized crossover experiment conducted in a controlled laboratory setting. Participants were exposed to heat (32°C during daytime; 30°C during nighttime) and thermoneutral conditions (26°C) for 8 h. Blood and urine samples were collected before and after each exposure to examine various renal biomarkers reflecting glomerular filtration function, tubular injury, and early kidney stress. Heat exposure affected both blood and urinary biomarkers of kidney function, with notable diurnal differences in renal responses. Daytime heat exposure primarily affected blood markers of glomerular filtration, increasing creatinine by 7.67% (95% CI: 4.73%-10.61%) and cystatin C by 3.05% (95% CI: 0.17%-5.93%), while reducing estimated glomerular filtration rate by 0.05% (95% CI: 0.02%-0.08%). In contrast, nighttime heat exposure predominantly elevated urinary biomarkers of early kidney stress, including insulin-like growth factor-binding protein 7 (58.40%, 95% CI: 27.66%-89.14%), kidney injury molecule-1 (47.25%, 95% CI: 18.91%-75.59%), and tissue inhibitor of metalloproteinases-2 (51.88%, 95% CI: 22.26%-81.51%). Moreover, increases in insulin-like growth factor-binding protein 7 were significantly greater at night than during the day. Sleep-related parameters, including sleep quality, duration, and heart rate variability, partially mediated nighttime heat effects on renal responses. These results indicated that heat exposure induced different diurnal patterns in renal responses.

Humans↗

PubMed2026

Ten-Year Update of Nurse Practitioner Service Impact on Patient and Health Service Outcomes in Emergency Care Settings-A Systematic Review.

AIMS: To provide a 10-year update on the best available evidence evaluating the impact of nurse practitioner services on cost, waiting times, patient satisfaction, representation rates, and length of stay in emergency and urgent care settings. DESIGN: Systematic review. DATA SOURCES: The search was completed on January 28, 2025, in Embase (Elsevier), Medline (EBSCOhost), CINAHL (EBSCOhost), Cochrane Library (Wiley), Emcare (Ovid), Web of Science Core Collection (Clarivate) and Scopus (Elsevier). The data range (2014-2024) was used to limit the search. METHODS: The search was conducted with results imported into Covidence. In Covidence, two reviewers conducted screening, data extraction, and quality appraisal of articles, and findings were analysed using a narrative synthesis approach. Eligible studies examined nurse practitioner services in emergency or urgent care settings, reporting outcomes of cost, waiting times, patient satisfaction, representation rates, and length of stay. RESULTS: Title and abstract screening were performed on 2329 records. Of these, 236 full-text articles were reviewed, and 17 underwent critical appraisal and data extraction. Narrative analysis of outcome measures yielded mixed results, with both favourable and unfavourable findings reported regarding nurse practitioner services. CONCLUSIONS: Global evaluation of nurse practitioner services in emergency care remains inconsistent. Nevertheless, emerging evidence supports their positive impact, particularly in improving patient outcomes. To effectively inform policy, workforce planning and clinical integration, there is a need for professional benchmarks that provide clear frameworks for the evaluation of patient-centred outcomes and operational impacts in emergency departments. IMPLICATIONS: Evidence related to nurse practitioner services in emergency and urgent care clinics highlights the positive impact of nurse practitioner services on patient wait times and satisfaction; however, there is limited and variable evidence of impact on health care costs and outcomes. IMPACT: This paper recommends that evaluating emergency nurse practitioner services requires homogeneous research using consistent professional benchmarks and evaluation frameworks. REPORTING METHOD: This systematic review follows the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. PATIENT OR PUBLIC CONTRIBUTION: This study did not include patient or public involvement in its design, conduct, or reporting. TRAIL REGISTRATION: PROSPERO 2025 CRD420250645148.

Humans↗

PubMed2026

New Horizons in the Development of Treatments for Substance Use Disorders.

Substance use disorders (SUDs) are a major public health problem in the United States and cause substantial morbidity and mortality. There are meaningful gaps in the available SUD treatment options, and the development of new therapies is urgently needed. While medications with U.S. Food and Drug Administration approval are available for alcohol, nicotine, and opioid use disorders, there are no approved pharmacotherapies for cannabis, cocaine, or methamphetamine use disorders. Behavioral treatments for SUDs have significant limitations in effectiveness and accessibility, and there is a need for the development of both new behavioral treatment options and new models of treatment delivery. The next generation of treatments for SUDs will likely come from a diverse set of interventions, including new drug classes, new technologies, and new methods of delivery.

Humans↗

PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗

PubMed2026

Cost-Effectiveness of Electronic Patient-Reported Outcome Measure Interventions in Cancer: Systematic Review and Parameter Extraction for Economic Modeling.

BACKGROUND: Complex digital interventions that integrate electronic patient-reported outcome measures (ePROM) into clinical practice in cancer have the potential to improve quality of life, increase survival, and reduce health resource use and costs. Such systems can help patients with cancer self-manage chemotherapy symptoms, reduce clinicians' workloads through automated decision support, and resolve problems earlier. However, more research on the cost-effectiveness of ePROM monitoring is needed. OBJECTIVE: This paper comprises two complementary components: (1) a systematic literature review summarizing and evaluating the quantitative and qualitative evidence related to the cost-effectiveness of ePROM monitoring and (2) a health economic model parameter extraction. We also conducted supplementary targeted searches and scoping to provide context to our findings. METHODS: We searched Ovid (including MEDLINE and Embase), Scopus, and the International Health Technology Assessment Database for original English-language papers published on or before March 2025 using search strings that combined terms related to ePROMs, health economics, and cancer/oncology. We included papers reporting health economic-related outcomes for ePROM interventions designed for adult cancer populations and excluded screening tools and conference abstracts. RESULTS: We included 34 publications from 27 unique studies and identified and analyzed 26 ePROM-integrated interventions within these. Most (23/26) of the included interventions explicitly described some form of alert handling and automated decision support based on remote ePROM monitoring. Of the 34 publications, 5 presented full cost-effectiveness analysis results, of which 3 were highly uncertain and lacked clear differences in costs and health outcomes between ePROMs and standard care; conversely, 2 presented strong evidence of cost-effectiveness due to quality-of-life improvements, reduced hospitalizations, and potentially more autonomy in health-related travel (eg, ePROM-monitored patients can drive or walk to the hospital instead of using taxis or ambulances). A further 5 publications reported partial health economic results (eg, cost-consequence and budget impact), of which 1 detected no difference in strategies; in contrast, 4 reported lower health resource use and costs of ePROMs, mainly due to hospitalization reductions. Overall, 12 of the 27 studies included a qualitative component but mostly focused on user experience and design-related themes; only 2 of these addressed economic-specific themes (eg, changes in workflow and resource use due to ePROM implementation and integration), indicating some potential for time saving due to ePROM monitoring. CONCLUSIONS: Some ePROM-integrated interventions demonstrated cost-effectiveness in cancer care, but the evidence base remains limited. Where evidence does exist, cost-effectiveness appears driven by reduced hospitalization and improved quality of life. Qualitative research within the included studies rarely addressed economic questions. We provide a detailed parameter extraction for use in future economic modeling and recommend research priorities, including quantitative mapping of ePROM symptom data onto health resource use patterns, and qualitative work exploring how ePROM implementation affects clinical workloads and patient-perspective costs.

Humans↗