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PubMed2026

Effectiveness and implementation of task-sharing cognitive-behavioral interventions for perinatal mental health: A systematic review and meta-analysis.

OBJECTIVE: To evaluate the effectiveness of cognitive-behavioral interventions (CBIs) delivered by nonspecialist providers (NSPs) on perinatal depressive (PND) and anxiety symptoms, and to narratively synthesize their implementation processes and reported implementation outcomes, including acceptability, feasibility, fidelity, cost, and sustainability. METHODS: We systematically searched eight databases from inception to April 8, 2025. Eligible studies were randomised controlled trials (RCTs) assessing CBIs delivered by NSPs for PND and/or anxiety. Two reviewers independently screened, extracted, and assessed trials. Meta-analyses employed random-effects models, with subgroup, sensitivity, meta-regression, and publication bias analyses conducted in Stata 18.0. Implementation processes and outcomes were reported as frequencies or percentages across trials. RESULTS: A total of 47 trials (11, 357 participants) were included in the systematic review, of which 37 trials (8,709 participants) were included for meta-analyses. CBIs were conducted in 12 countries. Nurses and midwives delivered 45% of CBIs. CBIs were associated with reduced PND post-intervention compared with control conditions (standardized mean difference [SMD] -0.49, 95% CI -0.63 to -0.35; I² = 86.8%). Limited evidence from four trials suggested a small sustained effect at 12 months (SMD -0.14, 95% CI -0.27 to -0.02; I² = 26.4%). Reductions in anxiety symptoms were observed immediately post-intervention (SMD, -0.45, 95% CI -0.65 to -0.25; I²=81%), but evidence for longer-term effects was limited. Subgroup analyses confirmed consistent effects across diverse settings, populations, and intervention characteristics. Reporting of implementation processes (e.g., training, supervision, fidelity) was limited, with only 23.4% of trials assessing fidelity and 10.6% evaluating costs. CONCLUSIONS: NSP-delivered CBIs showed beneficial effects on PND and anxiety, with generally encouraging evidence for acceptability and feasibility. However, evidence for sustained effects beyond the immediate post-intervention period remains limited. Future studies should strengthen long-term follow-up and improve reporting of implementation processes and outcomes, particularly in rural and adolescent perinatal populations, to inform scalable and equitable task-sharing models.

Humans↗

PubMed2026

Assessment and CommuniCation ExcelLEnce foR sAfe paTient outcomEs (ACCELERATE): A stepped-wedge cluster randomised trial evaluating the effectiveness of a nurse-led assessment and handover communication intervention on patient adverse events.

BACKGROUND: Patients continue to experience harm from undetected deterioration, falls and pressure injuries. We aimed to implement and evaluate an organisational, ward-level nurse-led assessment and communication intervention to proactively reduce patient adverse events. METHODS: A stepped-wedge cluster randomised Trial over 12-months was conducted at three metropolitan hospitals. Our intervention comprised a comprehensive, systematic patient assessment at shift commencement; a structured patient-centred bedside nurse-to-nurse clinical handover; and multidisciplinary communication consisting of nurse participation in medical ward rounds. Evidence-based implementation strategies informed intervention delivery to nine clusters (20-35 bed-wards with ≥70% permanent nurses) over three sequential 14-week steps. Routinely collected patient-level data were used to measure intervention effect. The primary outcome was a composite measure of medical emergency team calls, unplanned intensive care unit admissions, in-hospital falls; and stage 2-4 pressure injuries. Secondary outcomes were: individual measures of the primary outcome; nurse-reported perceptions of safety culture; organisational readiness to change; barriers to physical assessment; staff engagement; and patient-reported experience measures of safety and overall hospital experience. Analyses were adjusted for age, sex, hospital, pre/post intervention, and Trial step (fortnight), with random effects for ward and patient. RESULTS: There were 13,753 eligible admissions. No change was observed in the primary composite outcome measure (odds ratio (OR) [95% confidence interval (CI)]: 0.99 [0.77, 1.28]; p = 0.95). There was no significant difference in medical emergency team calls (OR [95% CI]: 1.02 [0.75, 1.39]; p = 0.91); unplanned intensive care unit admissions (OR [95% CI]: 1.35 [0.57, 3.20]; p = 0.50) and falls (OR [95% CI]: 1.53 [0.96, 2.45]; p = 0.07). However, stage 2-4 pressure injuries significantly decreased by 41% (OR [95% CI]: 0.59 [0.38, 0.93]; p = 0.02); a significant absolute effect improvement of 0.8% ([95% CI: 0.3%-1.3%], p < 0.01). There were statistically significant improvements in nurses' overall perceptions of Safety Attitudes (Pre: 74.6, Post: 79.7; p = 0.02), and the Organisational Readiness to Change subscales of, leader culture (Pre: 3.73, Post 3.91; p = 0.02), leadership behaviour (Pre: 3.85, Post: 4.11; p = 0.03), and general resources (Pre: 3.06, Post: 3.30; p = 0.03). A statistically significant decrease in Barriers to Physical Assessment (Pre: 2.48, Post: 2.24; p <0.001) and in six of seven sub-scales was observed. Patients' overall Measure of Safety remained high, but unchanged (Pre: 3.94 Post: 3.92; p = 0.07). CONCLUSION: The ACCELERATE Trial demonstrated that nurse-driven initiatives, emphasising structured physical assessments by nurses, patient-centred clinical handovers, and multidisciplinary communication, significantly: reduced pressure injuries; decreased nurses' perceived barriers to performing physical assessments; and improved leadership behaviour, communication, and ward safety culture perceptions. Results highlight the transformative potential of this approach, which now warrants testing at scale for broader implementation. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ID: ACTRN12621000265875.

Humans↗

PubMed2026

Access to palliative care in rural settings: A mixed-methods systematic review.

BACKGROUND: Rural populations experience persistent inequities in access to palliative care. Existing evidence often describes individual barriers separately, with less attention to how access breaks down across the care pathway or how different service configurations shape access. OBJECTIVES: To synthesise evidence on access to palliative care in rural settings and examine how access barriers, service models, and implementation conditions interact across the care pathway. METHODS: A mixed-methods systematic review using a convergent integrated approach searched nine databases (PubMed, Embase, CINAHL, Web of Science, Scopus, PsycINFO, CNKI, WanFang, SinoMed) from inception to 15 March 2026, supplemented by hand-searching. Eligible studies were primary qualitative, quantitative, and mixed-methods studies on access to palliative care for adults in rural or non-urban settings. Two reviewers independently screened studies, extracted data, and assessed quality using the Mixed Methods Appraisal Tool. Findings were mapped to the Levesque access framework, analysed using the updated Consolidated Framework for Implementation Research, and integrated through mixed-methods synthesis, with additional coding of service models. RESULTS: Thirty-four studies were included, of which 26 were conducted in high-income countries and eight in low- and middle-income countries. Service configurations included specialist or hospice-oriented care, generalist or primary-care-oriented care, mixed specialist-generalist models, home-based and caregiver-centred care, nurse-coordinated services, telehealth-supported care, and community or implementation-oriented approaches. Access broke down cumulatively across four interdependent stages: recognition, entry, reach, and use and continuity, with affordability constraining every stage. Recognition was limited by low awareness, poor service visibility, and delayed identification of need. Entry was shaped by stigma, trust, family expectations, and unclear referral processes. Reach was constrained by distance, transport, workforce shortages, limited specialist capacity, and weak infrastructure. Use and continuity were affected by fragmented coordination, weak transitions, unstable follow-up, and reliance on family caregivers. Access problems varied across service configurations. Evidence on service innovations was methodologically less certain, and the overall evidence base remained concentrated in high-income countries. CONCLUSIONS: Access to palliative care in rural settings is best understood as a pathway and service-configuration problem rather than simply a deficit in service availability. Improving access requires earlier recognition, clearer referral routes, stronger specialist-generalist and nursing links, better support for family caregivers, and greater attention to affordability, continuity, and rural settings with limited resources. REGISTRATION: International Prospective Register of Systematic Reviews: CRD420261340783.

Health Services Accessibility↗

PubMed2026

Implementing a novel digital health platform for self-management of postmenopausal osteoporosis: A qualitative study of user experiences, perspectives and implementation outcomes.

BACKGROUND: Osteoporosis self-management requires scalable support, and digital health platforms may meet this need. This study aimed to characterise the experiences and perspectives of postmenopausal women who participated in a 12-month randomised controlled trial (RCT) of a digital voice assistant (DVA) delivered osteoporosis self-management intervention, and to assess key implementation outcomes. METHODS: This was a qualitative analysis of interviews with postmenopausal women from the intervention arm (DVA group) of the RCT. The DVA program broadcast education videos, medication reminders, home-based exercise, nutrition advice and monthly quizzes through a DVA device. Semi-structured interviews were recorded, transcribed and managed in NVivo through reflexive thematic analysis, guided by the Practical Planning for Implementation and Scale-Up and Proctor's implementation outcome taxonomy frameworks. Evidence weighting summarised participant coverage and code density. RESULTS: Twenty-two of 25 (88%) DVA group participants completed semi-structured interviews. Thematic analysis identified seven themes mapped to Proctor's implementation outcomes. Evidence weighting indicated strong support for the intervention's appropriateness and acceptability, moderate support for its adoption, fidelity, feasibility and sustainability, and limited support for costs. Participants valued clear audiovisual guidance, conversation-based interactions with natural language, and flexible home-based access to self-management. CONCLUSION: Digital health platforms for osteoporosis self-management appear feasible, acceptable and sustainable among postmenopausal women. Findings indicate that these platforms are approaching readiness for evaluation in implementation-focused settings, contingent on streamlined content, reliable delivery modalities, accessible user support, clear privacy regulations and pragmatic pricing models.

Humans↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Medication safety in older adults in India: an integrative PhD synthesis of direct evidence and contextual implementation evidence.

BACKGROUND: Unsafe medication practices among older adults are an important global health concern, particularly in low- and middle-income countries where multimorbidity, fragmented care, self-medication, and informal healthcare provision intersect. OBJECTIVE(S): To synthesize direct evidence on medication safety among older adults in India and contextual evidence on deprescribing and community-level provider interventions relevant to safer medication use. METHODS: This PhD synthesis integrates four studies: a record-based cross-sectional study on polypharmacy and cardiovascular autonomic function in Kolkata; a six-city community study of 600 Indian older adults; a systematic review and meta-analysis on deprescribing preventive medications in frail or end-of-life older adults; and a systematic review of informal healthcare provider interventions in low- and middle-income countries. Studies I-II provided direct Indian older-adult evidence, while Studies III-IV provided indirect contextual evidence for their optimization and implementation. RESULTS: Polypharmacy was associated with higher anticholinergic burden and numerically higher cardiac autonomic neuropathy although residual confounding limits causal interpretation. In the multicity study, one-third had polypharmacy, while potentially inappropriate medications, prescribing omissions, and self-medication were common. Risks were higher with multimorbidity, recent hospitalization, care transitions, or living alone. Deprescribing showed no statistically significant increase in mortality, hospitalization, or major cardiovascular events, but heterogeneity was high and certainty low to very low. Informal-provider interventions showed the potential to improve knowledge, referral, case management, and medication-related practices. CONCLUSIONS: Medication safety among older adults in India requires an integrated continuum approach, but direct evidence supports only some components and implementation strategies that need prospective evaluation.

Humans↗

PubMed2026

Implementation of Mobile Health Intervention Targeting Belongingness and Burdensomeness: An Ecological Momentary Assessment Study of Self-Injurious Thoughts and Behaviors in LGBTQ+ Individuals.

OBJECTIVE: The goal of this paper was to test a mobile health intervention designed to reduce self-injurious thoughts and behaviors in LGBTQ+ individuals. The intervention consisted of brief messages aimed at increasing feelings of belongingness and meaning. METHOD: We recruited LGBTQ+ individuals (N = 55) with past-month self-injurious thoughts and/or behaviors. Participants completed 14 days of ecological momentary assessment (EMA) of minority stress, thwarted belongingness, perceived burdensomeness, and self-injurious thoughts and behaviors. Then, participants were randomly assigned to receive brief messages designed to instill belongingness and meaning/purpose, or no intervention for 14 days. Then, participants completed an additional 14 days of EMA. RESULTS: Our results showed that participants in the control condition had significant increases in self-injurious thoughts and planning over time, whereas those in the intervention condition showed no significant change. For self-injurious behavior, thwarted belongingness, and perceived burdensomeness, there were significant decreases in the intervention condition, but no changes in the control condition. CONCLUSIONS: These results provide support for the interpersonal theory of suicide and indicate a potentially scalable mobile health intervention. PUBLIC HEALTH SIGNIFICANCE: This paper found evidence that a brief mobile health intervention reduced suicidal and non-suicidal self-injurious thoughts and behaviors among LGBTQ+ individuals.

Humans↗

PubMed2026

Effects of hospital planning reforms on access, costs, efficiency, and quality of care in OECD countries: Systematic review and meta-analysis.

BACKGROUND: Many OECD countries have implemented hospital planning reforms to rising healthcare costs, demographic changes, and concerns about access, efficiency, and quality of care. Despite broad implementation, evidence on effectiveness remains fragmented and country-specific. OBJECTIVE: To synthesize evidence on the effects of hospital planning reforms aross four outcome domains: access, costs, efficiency, and quality of care. METHODS: We conducted a systematic review following Cochrane methodology, searching PubMed and Web of Science (January 2000 - September 2025). Studies were categorized into four intervention types - centralization, minimum volume requirements (MVR), performance-based targets, and governance and ownership restructuring. Risk of bias was assessed using Joanna Briggs Institute checklist for quasi-experimental designs. Where data permitted, random-effects meta-analyses pooled standardized mean differences (SMD) for access and efficiency and risk differences (RD) for quality outcomes. RESULTS: 26 studies from 12 countries were included. Centralization increased patient travel distances and reduced length of stay (SMD -0.09, 95% CI -0.17 to -0.01) and complications (RD -14.52 pp, -25.95 to -3.09), and, jointly with performance-based targets, 30-day readmissions (RD -0.43 pp, -0.65 to -0.22). Mortality effects varied by timepoint and intervention: short-term endpoints were largely non-significant, whereas 90-day mortality was reduced under centralization (RD -0.80 pp, -1.25 to -0.35) and 60-day mortality under MVR (RD -2.00 pp, -2.82 to -1.18). Survival was non-significant throughout. No study examined costs. CONCLUSION: The absence of cost evidence is a critical gap. Substantial heterogeneity reflects variation in reform design and context, underscoring the need to interpret findings by intervention and country conditions.

Humans↗

PubMed2026

Artificial intelligence for dental caries detection: An umbrella review.

Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.

Dental Caries↗

PubMed2026

Identifying stakeholder behaviors for competency-based pharmacy education: A stage 1 behavior change wheel analysis.

INTRODUCTION/OBJECTIVES: Competency-Based Pharmacy Education (CBPE) is a strategic priority for preparing graduates to meet evolving healthcare needs. However, efforts to implement CBPE can stall due to behavioral challenges among faculty, administrators, preceptors, and learners. This study aimed to apply Stage 1 of the Behavior Change Wheel (BCW) to identify stakeholder-specific behaviors and associated determinants needed to implement the five core components of CBPE. METHODS: A multi-method approach grounded in the BCW, the Capability, Opportunity, Motivation - Behavior (COM-B) model, and the Theoretical Domains Framework (TDF) was used. Data were gathered through (1) targeted literature review; (2) structured focus groups with competency-based education experts and pharmacy education stakeholders; and (3) an iterative consensus process. Behaviors were mapped to the five CBPE components: (1) defined competencies, (2) developmental progression, (3) tailored instruction, (4) authentic experiential learning, and (5) programmatic assessment, and then mapped to COM-B and TDF constructs. RESULTS: Over fifty stakeholder-specific behaviors were identified and specified across the CBPE framework. This revealed shared barriers such as limited instructional design knowledge (psychological capability), insufficient assessment of infrastructure (physical opportunity), and misaligned professional identity (reflective motivation). Key TDF domains included knowledge, environmental context, beliefs about capabilities, and professional roles. The behavioral problem statements, specifications, and determinants were identified to support future intervention planning. CONCLUSION: This Stage 1 analysis provides a behaviorally grounded foundation for CBPE implementation by identifying stakeholder behaviors and conditions that enable change. These findings will inform the development of readiness-to-change assessments and targeted interventions (BCW Stages 2 and 3), supporting scalable and sustainable CBPE transformation in pharmacy education.

Education, Pharmacy↗

PubMed2026

Hyper-oncotic albumin administration reduces mortality in acute Respiratory Distress Syndrome compared to crystalloid: a systematic review and meta-analysis.

BACKGROUND: To evaluate the association between albumin administration as volume replacement and mortality in adult ARDS patients, we performed this meta-analysis and trial sequential analysis (TSA). METHODS: We searched databases including PubMed, Science Direct, Scopus, Web of Science databases and Cochrane Central Register of Controlled Trials up to 12 December 2024. We screened trials that included adult ARDS patients and compared albumin with crystalloid. The 28-day mortality served as the primary endpoint, while the oxygenation change, the length of ICU stay and the length of hospital stay were designated as secondary outcomes. To clarify the differing concentrations of albumin, we formed two distinct subgroups: the hyper-oncotic albumin subgroup (≥20%) and the iso-oncotic albumin subgroup (4%∼5%). Statistical synthesis was performed with Cochrane Review Manager 5.4.1, employing random-effects models. To mitigate random errors, TSA was implemented with α = 0.05 and β = 0.20 parameters. RESULTS: The analysis incorporated 5 publications: 3 randomized controlled trials (RCTs) and 2 non-randomized studies (NRSs). Overall mortality was lower in the albumin group (33.2%, 97/292) than in the crystalloid group (44.9%, 133/296) (OR = 0.61, 95%CI 0.43-0.85, p = 0.004). RCTs (n = 204) showed no benefit (OR = 0.83, p = 0.54), but NRSs (n = 384) demonstrated reduced mortality (OR = 0.52, p = 0.002). Hyper-oncotic albumin was associated with lower mortality in NRSs (OR = 0.40, p = 0.02) but not in RCTs (OR = 0.74, p = 0.57). Iso-oncotic albumin showed no benefit (OR = 0.88, p = 0.72). Regarding the impact of albumin on oxygenation, significant improvements in oxygenation were observed only on the first (p = 0.05) and second days (p < 0.0001). The TSA indicated a continued need for high-quality RCTs. CONCLUSIONS: Our analysis suggests that hyper-oncotic albumin may reduce mortality and improve early oxygenation in ARDS patients compared to crystalloids. Larger RCTs are urgently needed to validate these findings and define their potential role in clinical management.

Humans↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Safety and efficacy of Meridian sinew tuina (MST) for post-surgical upper limb lymphedema: a systematic review and meta-analysis.

BACKGROUND: Complex Decongestive Therapy (CDT) is the non-operative standard for breast cancer-related lymphedema (BCRL), but many patients experience persistent subcutaneous stiffness, pain, and restricted mobility. This study systematically reviews the safety and clinical efficacy of Meridian Sinew Tuina (MST) protocols for BCRL. METHODS: Global and regional databases (PubMed, Cochrane Library, Embase, Web of Science, CNKI, Wanfang, VIP) were searched from inception to January 15, 2026, with alerts monitored through April 30, 2026. Randomised controlled trials (RCTs) evaluating MST (deep tissue mobilisation along the six-hand meridian sinew [Jingjin] lines via plucking, kneading, and pressing) were included. Two reviewers independently extracted data, evaluated risk of bias using Cochrane RoB 2, and assessed evidence certainty via GRADE using a random-effects model. RESULTS: Fifteen RCTs were included. For the primary anthropometric outcome, MST significantly reduced upper limb circumference compared to controls (SMD = 1.59; 95% CI: 1.44 to 1.74; Z = 20.81; p < 0.0001; I2=0.0%; N = 924; GRADE: Moderate certainty). The Clinical Response Efficacy Rate (≥ 30% swelling reduction and symptom relief) favoured MST (RR = 1.69; 95% CI: 1.54 to 1.87; Z = 10.62; p < 0.0001; I2=0.0%; N = 1,114; GRADE: Moderate certainty). Trial Sequential Analysis confirmed sample size sufficiency. For secondary outcomes (N = 924; GRADE: Low to Very Low certainty due to performance bias and clinical heterogeneity), MST showed favourable 3-month improvements in DASH functional scores (SMD = -1.81; 95% CI: -2.11 to -1.51; I2=45.1%), pain intensity (SMD = -2.44; 95% CI: -2.93 to -1.95; I2=50.4%), and quality of life (SMD = 1.04; 95% CI: 0.79 to 1.29; I2=0.0%). No serious adverse events occurred. CONCLUSIONS: MST protocols are associated with favourable short- and mid-term reductions in upper limb swelling. However, confidence is tempered by unblinded performance bias and control group variations. MST cannot be unconditionally recommended for standalone implementation but represents a promising, optional supportive adjunctive intervention within oncological rehabilitation.

Humans↗

PubMed2026

Feasibility and barriers to same-day physical therapy following lumbar fusion surgery.

OBJECTIVE: To evaluate the feasibility of same-day (postoperative day 0; POD0) physical therapy (PT) following lumbar fusion and to identify factors associated with failure to participate. METHODS: This retrospective study analyzed prospectively collected data from patients undergoing single-level posterior spinal fusion (PSF), with or without anterior (ALIF) or lateral (LLIF) interbody fusion, between January and December 2024 at a single institution. A standardized POD0 PT protocol was implemented for eligible patients. Patients were categorized into two groups: successful POD0 PT (ambulatory on POD0) and unable to participate. Demographic and surgical variables were compared between groups. Reasons for inability to participate were recorded and categorized. RESULTS: Among 129 patients in whom POD0 PT was attempted, 84 (65%) successfully participated, while 45 (35%) were unable. There were no significant differences in age, sex, BMI, ASA class, operative time, estimated blood loss, or surgical approach between groups. Patients who successfully completed POD0 PT had a significantly shorter hospital length of stay compared to those who did not (3.4 ± 1.6 vs 5.8 ± 2.9 days, P < 0.001), with no differences in complication rates, discharge disposition, emergency department visits, or reoperation rates. The most common barriers to POD0 PT were postoperative pain, medical issues (e.g., orthostatic hypotension, nausea, dizziness), and anesthesia-related somnolence. Less common factors included postoperative restrictions and logistical issues such as brace availability. CONCLUSIONS: POD0 PT following lumbar fusion is feasible in the majority of patients and is associated with a shorter hospital stay without increased complications. Failure to participate was not associated with the baseline patient or surgical characteristics evaluated in this study. Instead, the most common barriers were postoperative pain, transient medical issues, and anesthesia-related somnolence, suggesting that optimization of modifiable perioperative factors may improve the implementation of POD0 PT.

Humans↗

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

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

Artificial intelligence-supported double reading in European population breast cancer screening: A systematic review and meta-analysis of prospective programs.

BACKGROUND: Most European population mammography screening programs rely on double reading with arbitration, a model that delivers mortality benefit but is increasingly challenged by radiologist workload, variable specificity, and interval cancers. Artificial intelligence (AI) is being evaluated to support or optimize these established European screening pathways. PURPOSE: To synthesize prospective or program-embedded evaluations of AI conducted within European-style population screening programs and to estimate exploratory program-level absolute risk differences (RDs) per 1000 examinations for cancer detection rate (CDR) and recall. MATERIALS AND METHODS: We performed a prespecified, focused evidence synthesis of three large studies embedded within routine population screening programs operating under European-relevant workflows: MASAI (randomized AI-supported risk triage within a national program), ScreenTrustCAD (prospective paired-reader evaluation with AI as an independent reader in a double-reading framework), and PRAIM (nationwide decision-referral implementation). Outcomes were harmonized as AI-control RDs per 1000 examinations. Random-effects pooling used Hartung-Knapp-Sidik-Jonkman models. For the paired-reader design, sensitivity analyses applied a Kish effective sample-size approach across plausible within-examination correlations (ρ = 0.3-0.8). Positive predictive value (PPV) and workflow/time outcomes were summarized descriptively. RESULTS: Across 597,419 examinations, the pooled CDR RD was +0.9 per 1000 (95% CI -0.0 to +1.8; I2 ≈ 12%), consistent with a modest directional increase with borderline statistical uncertainty. The pooled recall RD was -0.6 per 1000 (95% CI -3.1 to +2.1; I2 ≈ 41-43%), indicating no consistent recall increase across screening programs. Where reported, PPV was higher with AI-supported screening. Efficiency signals included 44.3% fewer total readings in MASAI and shorter reading times for AI-normal examinations in PRAIM; in PRAIM, a program-level safety-net mechanism recovered 204 cancers that would otherwise have been missed. CONCLUSION: In European population screening programs characterized by double reading and arbitration, prospective program-embedded evidence suggests that AI integration may yield a small absolute increase in cancer detection (≈1/1000) without a consistent increase in recall, alongside improved PPV and efficiency signals. These findings suggestAI primarily as a complementary reader within European screening workflows, with implementation requiring explicit quality assurance and monitoring of interval cancers and stage distribution.

Humans↗

PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗