Search Results (1,834)

(1) Background: Medication-related problems (MRPs) are a significant burden on health care systems. Pharmacists play an important role in preventing and reducing MRPs through clinical review, education, and policy governance. This study analyzed pharmacist interventions within a 92-bed neonatal clinical care unit to better understand MRPs and guide targeted medication safety initiatives. (2) Methods: All pharmacist interventions documented in REDCap^® between 1 July 2022 and 30 June 2025 were analyzed identifying MRP incidence, types, and acceptability following interventions. (3) Results: A total of 873 pharmacist interventions were recorded during the study period. The most common MRPs were related to dosing errors (320/873, 36.7%), compliance with hospital policy (152/873, 17.4%), no indication apparent (106/873, 12.1%), drug interactions (66/873, 7.6%), and inadequate laboratory monitoring (40/873, 4.6%). Of these, 545/873, 62.4% were accepted by prescribers, while 228/873, 26.1% had unknown outcomes at the time of data entry. 343/873, 39.3% of interventions documented were from the Neonatal Intensive Care Unit, involving medications such as gentamicin (n = 46/343, 13.4%), benzylpenicillin (n = 37/343, 10.8%), caffeine (n = 34/343, 9.9%), parenteral nutrition (n = 23/343, 6.7%), and morphine (n = 16/343, 4.7%) and meropenem (n = 16/343, 4.7%)). (4) Conclusions: Regular analysis of pharmacist interventions provides valuable insights into local MRP trends and highlights opportunities for quality improvement and education. Full article

To solve common defects such as warpage deformation, interface debonding, and uneven filling during the two-color injection molding of medical goggles while meeting their multi-performance requirements, including high light transmittance, impact resistance, chemical corrosion resistance, and structural stability, this study conducts research on the process optimization of two-color injection molding. Firstly, based on the principle of material compatibility and Moldflow simulation, a suitable material combination was selected: the first-shot frame adopts Apec 1745 PC material, and the second-shot lens uses Makrolon 2858 PC material, which effectively avoids the risk of interface non-fusion. Subsequently, a high-precision 3D simulation model was established using Moldflow software, and the injection sequence of “frame first, lens second” was optimized and determined. A gating system with double-gate (for the frame) and single-gate side feeding (for the lens), as well as a cooling system with an 8 mm diameter, was designed, and all key indicators of mesh quality meet the simulation requirements. Taking the mold and melt temperatures, holding pressures, and holding times of the two shots as design variables and warpage deformation as the optimization objective, sample data were obtained through an L₃₂ (7⁴) orthogonal test. A BP neural network was constructed to describe the nonlinear relationship between parameters and quality, and the Sparrow Search Algorithm (SSA) was combined to optimize the weights and thresholds of the network, forming a BP-SSA intelligent optimization model. The results show that the mean absolute percentage error (MAPE) of the proposed model is only 2.28%, which is significantly better than that of the single BP neural network (14.36%). The optimal process parameters obtained by optimization are a mold temperature of 130 °C, first-shot melt temperature of 311 °C, second-shot melt temperature of 310 °C, first-shot holding pressure of 83 MPa, second-shot holding pressure of 70 MPa, first-shot holding time of 14 s, and second-shot holding time of 8 s. Simulation and mold test verification indicate that after optimization, the warpage deformation of the goggles is reduced to 0.8956 mm (simulation) and 0.944 mm (measured), with a relative error of only 5.4%, which is 67.9% lower than the initial simulation result. The integrated method of “material selection—CAE simulation—orthogonal test—BP-SSA intelligent optimization” proposed in this study provides technical support for the high-precision manufacturing of thin-walled transparent multi-material medical products. Full article

Lung cancer remains one of the most formidable health challenges globally, with significant morbidity and mortality rates. Despite advancements in diagnostic and treatment technologies, the disease’s high prevalence, late-stage detection, and complex variations continue to hinder effective management. Early detection and accurate diagnosis play a pivotal role in improving survival rates. Crucially, the clinical and translational relevance of AI-based prediction lies in its potential to significantly reduce the incidence of late-stage diagnoses, thus increasing the chance of successful intervention. Lung cancer was first identified by medical professionals in the mid-19th century. Today, cancer remains a significant global health challenge, affecting an estimated 14 million individuals annually and causing 8.2 million fatalities worldwide. Lung cancer ranks among the leading causes of death associated with cancer. This research aims to bridge gaps in lung cancer diagnosis by exploring various learning methodologies. By focusing on studies from the last 10 years, this survey provides a contemporary understanding of the field, emphasizing the importance of automated diagnostic systems in reducing human error and improving efficiency. The selection of relevant research is based on a rigorous methodology, including specific inclusion and exclusion criteria, which are later discussed in detail with supporting figures and comparative data. Ultimately, this work underscores the critical need for innovative diagnostic solutions and comprehensive screening programs to combat lung cancer, save lives, and advance the field of medical research. Full article

Background/Objectives: Medication errors (MEs) in intensive care units (ICUs) remain a persistent threat to patient safety. A significant surveillance gap exists where traditional voluntary reporting detects as few as 0.02 MEs per patient-day, leaving approximately 98% of errors invisible to standard audits. This review critically examines how artificial intelligence (AI) and implementation science can bridge this gap through a proposed five-layer Intelligent Safety Stack. Methods: We conducted a critical narrative review of the peer-reviewed literature published between 2000 and 2025, synthesising evidence across medication safety, predictive analytics, generative AI, engineering controls, and sociotechnical frameworks. Results: Reported ME incidence varies widely (1.32% to 31.7%) due to the profound methodological heterogeneity. To achieve sustainable safety, we propose a five-layer framework: (1) Standardised Ontology (e.g., NCC MERP) to establish ground-truth data; (2) Intelligent Surveillance to identify and monitor high-risk patients; (3) Signal Optimisation to filter noise and reduce alert fatigue; (4) Generative Stewardship to automate reconciliation at transitions of care; and (5) Engineering Controls (smart pump interoperability and NRFit™), which have been shown to reduce administration errors by up to 54.8%. Conclusions: Isolated error counting is insufficient. Sustainable medication safety in the ICU involves a sociotechnical fusion of the Intelligent Safety Stack with success measured by rescue rates rather than error prevalence alone. Full article

The paper presents a novel method for automated image processing that combines pyramidal generalized contour preprocessing with parallel–hierarchical segmentation, integrating adaptive multilevel thresholding to enhance segmentation accuracy and robustness. The proposed approach is designed to overcome the limitations of traditional methods—whose performance declines under variations in brightness, surface texture, and noise—by enhancing image contrast and structural defect detection, thereby reducing diagnostic errors and misclassification risks. To achieve these objectives, the implementation utilizes multilevel adaptive thresholding, enabling step-by-step segmentation refinement and the extraction of informative regions using three-level coding (positive, negative, and neutral elements). In conjunction with parallel–hierarchical (PH) transformations and high-frequency filtering, the method enhances image contrast, enables more accurate detection of structural defects, and reduces the number of false positives. Experimental results demonstrate a 10–15% improvement in segmentation accuracy compared to classical methods such as region-growing techniques. Furthermore, correlation analysis between automatic and manual segmentation results demonstrated a high degree of consistency, with a correlation coefficient of 0.95–0.99, indicating the reliability and reproducibility of the developed approach. The proposed method is distinguished by its high processing speed, computational simplicity, and versatility of application, ranging from medical thermography for pathological diagnostics to real-time monitoring of railway infrastructure. The practical significance of these results lies in advancing automation, reducing decision-making errors, and ensuring greater reliability of technical and medical control systems. Full article

Background and Objectives: Attention-Deficit Hyperactivity Disorder (ADHD) is a neurological condition characterized by cognitive task difficulty, impulsivity, hyperactivity and loss of attention. This study compared four approaches for improving attention and related skills in school-age children: COGO Brain–Computer Interface (BCI)-based attention training, methylphenidate, citicoline, and their combined use. Materials and Methods: A quasi-experimental pre–post design was used with four groups: COGO + methylphenidate (n = 44), COGO + citicoline (n = 44), COGO-only (n = 44), and citicoline-only (n = 42). Children completed baseline and post-treatment assessments, including the CPT-3 and several behavioral and emotional rating scales. Analyses included paired t-tests, ANCOVA, and repeated-measures ANOVA, adjusting for age. Results: The strongest improvements appeared in the COGO + methylphenidate group, especially in measures of sustained attention and reaction time consistency. The COGO + citicoline group showed clearer gains in inhibitory control (fewer commission errors) and reductions in anxiety/emotional symptoms. The COGO-only and citicoline-only groups showed little to no measurable change. Despite these within-group patterns, there were no significant differences between groups on CPT-3 outcomes or behavioral/emotional scales. Conclusions: This trial showed that combining COGO-based attention training with medication is both feasible and well-tolerated in children with attention and executive function difficulties. Moreover, the integrated approach produced measurable improvements across attentional performance and behavioral regulation domains. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

Background: Treating depression and anxiety in adolescents can be challenging due to interindividual variability in medication response. With current trial-and-error prescribing practices, adolescents may undergo multiple medication changes over months or years before an effective and tolerated drug and dose are identified. Pharmacogenomic (PGx) testing can identify interindividual differences in drug metabolism, and evidence supporting PGx-guided prescribing in adults with mental disorders is growing. However, its impact on pediatric psychotropic prescribing remains underexplored. Methods: This is a protocol for a parallel-arm, multicentre, randomized controlled trial. Canadian adolescents aged 12–17 years who are initiating or switching a selective serotonin reuptake inhibitor (SSRI) for depression and/or an anxiety disorder under physician care are eligible. A total of 452 participants will be randomized 1:1 to PGx-guided SSRI prescribing (experimental) or SSRI prescribing based on current practice guidelines (control). Participants, caregivers, prescribing clinicians, outcome assessors, and investigators will be blinded to treatment allocation. Dual primary outcomes are symptom remission at 12 weeks, measured with the Quick Inventory of Depressive Symptomatology–Adolescent (QIDS-A17-SR) and the Screen for Child Anxiety Related Disorders (SCARED). Secondary outcomes, assessed at 4, 8, and 12 weeks, include participant- and physician-rated changes in depressive and anxiety symptoms, role functioning, health-related quality of life, health care utilization, cost-effectiveness, side-effect burden, medication burden, and adherence. Multiple testing will be addressed using the Hochberg method, and a parallel gated analysis will account for non-actionable genotypes. Secondary analysis will estimate minimal clinically important differences for symptom and role-functioning change with PGx-guided therapy. Discussion: At the time of writing, 36 participants have consented and been randomized to an intervention. This trial will evaluate whether PGx-guided prescribing improves symptom remission in adolescents treated with SSRIs. If efficacious, results should be interpreted with existing pediatric pharmacokinetic, observational, and adult trial data to inform PGx use in managing pediatric anxiety and depressive disorders. Full article

Background: Venous catheters are standard devices in clinical practice. However, their use is not exempt from possible errors and complications. In addition, using them effectively is key to avoiding complications such as infection or phlebitis. Objectives: To determine the frequency of appropriate venous catheters chosen based on the drug and treatment duration in hospitalized patients in a region with 154,000 inhabitants. Methods: A cross-sectional design was carried out between 14 and 28 February 2020, in patients with a peripheral or central intravenous catheter admitted to the acute care unit. Variables collected were related to the catheters, patients, and nurses. Results: One hundred and eighty-eight patients were included, with 319 catheters inserted by 68 nurses. Seventeen patients (8.8%) were ruled out due to the lack of data on the medication administered. Finally, data from 171 patients were included in the final analysis, with 297 catheters inserted. Of them, 246 catheters (82.8%) were inadequate. Discussion: In this point-prevalence study, catheter inadequacy affected more than four out of five catheters and was mainly linked to the use of peripheral catheters for high-risk IV medications and/or for treatments extending 7 days or more. Conclusion: The selection of venous catheters in acute care units is not usually adequate since many peripheral catheters are placed in patients who require intravenous medication during a prolonged period or who are receiving risk medication. Full article

This work presents Recurrence with Correlation Network(RWCNet), a novel multi-scale recurrent neural network architecture for medical image registration that integrates core principles from optical flow, including correlation volume computation and inference-time instance optimization. In evaluations on the large-displacement National Lung Screening Test (NLST) dataset, RWCNet exhibited superior performance (total registration error (TRE) of 2.11 mm) compared to other deep learning alternatives, and achieved results on par with variational optimization techniques. In contrast, on the OASIS dataset, which is characterized by smaller displacements, RWCNet achieved an average Dice similarity of 81.7%, representing only a modest improvement over other multi-scale deep learning models. Ablation experiments showed that multi-scale features consistently improved performance, whereas the correlation volume, number of recurrent steps, and inference-time instance optimization had large impacts on performance within the large-displacement NLST dataset. The performance of RWCNet compared to approaches that use instance optimization show that deep learning-based methods can find local minima that escape instance optimization methods. The results highlight the need for algorithm hyperparameter selection that adjusts with the dataset characteristics. RWCNet’s promising results may improve registration accuracy and computation efficiency, enabling many potential applications such as treatment planning, intra-procedural guidance, and longitudinal monitoring. Full article

Unlike conventional positioning systems that rely on multiple sensors, the positioning system proposed in this study uses a single anisotropic magnetoresistive (AMR) sensor to measure the magnetic field of a target permanent magnet. This approach significantly reduces the system hardware cost and complexity, facilitating the miniaturization of positioning systems. Leveraging a BP neural network model, which is shown to be fast and accurate, the positioning system obtains the real-time magnetic field of the target magnet using a single sensor, subsequently converting three-axis magnetic field data into coordinate information to achieve real-time tracking and localization. The results show that the root mean square errors (RMSEs) for the X and Z axes in the simulation are 0.27 mm and 0.26 mm, respectively, while the RMSEs for the X, Y, and Z axes in the actual test are 0.83 mm, 1.15 mm, and 0.85 mm, respectively. It is also observed that the positioning error correlates with variations in the magnetic field with respect to position, which originate from the strong distance-dependent nonlinearity of the magnetic field. This method not only reduces hardware costs but also maintains accuracy. It is particularly well-suited to applications requiring high-precision positioning and tracking, achieving millimeter-level accuracy within a volume of 50 × 40 × 40 mm³. It has potential applications in aerospace intelligent connectors, medical devices and automation systems, where space and signal lines are limited. Full article

Low-cost, smartphone-based thermal cameras offer unprecedented accessibility for physiological monitoring, yet their validity and reliability for absolute skin temperature measurement in clinical settings remain contentious. This study aims to quantify the agreement and repeatability of a widely used smartphone thermal camera, the FLIR One Pro, against a consumer-grade, non-contact infrared thermometer, the iHealth PT3. A method comparison study was conducted with 40 healthy adult participants, yielding a total of 2400 temperature measurements. Skin temperature of the hand dorsum was measured concurrently with the FLIR One Pro and the iHealth PT3. The protocol involved two rounds: Round 1 (R1) in a stable, static environment to assess baseline repeatability, and Round 2 (R2) in a dynamic environment mimicking clinical repositioning. The performance of the instruments was compared using paired t-tests for mean differences and Bland–Altman analysis for assessing agreement. The iHealth PT3 demonstrated superior precision, with an average intra-participant standard deviation (SD) of 0.030 °C in R1 and 0.092 °C in R2. In stark contrast, the FLIR One Pro exhibited significantly higher variability, with an average SD of 0.34 °C in R1 and 0.30 °C in R2. Bland–Altman analysis revealed a substantial mean bias of −1.42 °C in R1 and −1.15 °C, with critically wide 95% limits of agreement ranges of ≈6 °C. The substantial systematic bias and poor agreement of the FLIR One Pro far exceed both its manufacturer-stated accuracy and clinically acceptable error margins for absolute temperature measurement. To further examine whether calibration could mitigate these deficiencies, we applied a suite of ten machine learning regressors to map FLIR readings onto iHealth PT3 values. Calibration reduced systematic bias across all models, with Quantile Gradient-Boosted Regression Trees achieving the lowest MAE (1.162 °C). The Extra Trees model yielded the lowest RMSE (1.792 °C) and the highest explained variance ($R^2$ = 0.152), yet this relatively low value confirms that the device’s high intrinsic variability limits the effectiveness of algorithmic correction. As such the device has limited utility for longitudinal patient monitoring or for diagnostic decisions that rely on precise, absolute temperature thresholds. These findings inform medical practitioners in low-resource settings of the profound limitations of using this device as a standalone clinical thermometer and emphasize that algorithmic correction cannot compensate for fundamental hardware and measurement noise constraints. Full article

Background: Large language models (LLMs) such as DeepSeek-V3, Google Gemini 2.0 Pro, and ChatGPT-4o are increasingly used by patients seeking online medical information. However, their accuracy, reliability, and reproducibility in patient-facing content related to chest wall deformities (CWD) remain unclear. This study aimed to compare the performance of three contemporary LLMs in generating information on pectus excavatum, pectus carinatum, and related thoracic deformities. Methods: Eighty patient-facing questions were developed across eight thematic domains and independently submitted to each model using newly created accounts over two consecutive days. Accuracy was assessed using a validated four-point rubric by blinded physiatrists, and reproducibility was evaluated using agreement metrics and weighted Cohen’s kappa. Results: ChatGPT-4o achieved the highest overall accuracy (median score: 1.20), the greatest proportion of fully accurate responses, and the lowest hallucination rate (5.0%). Gemini showed intermediate accuracy, while DeepSeek-V3 demonstrated the lowest accuracy and highest hallucination rate (11.25%). Across all models, general-information and quality-of-life domains had the best performance, whereas treatment-related questions showed the most errors. Reproducibility was highest for ChatGPT-4o (weighted κ = almost perfect), followed by Gemini and DeepSeek-V3. Inter-rater reliability was substantial (Fleiss’ κ = 0.69). Conclusions: Contemporary LLMs can generate largely accurate and reproducible patient-facing information on CWD, with ChatGPT-4o showing the strongest overall performance. This study provides the first domain-specific comparative evaluation of LLMs in CWD and integrates reproducibility analysis alongside accuracy and reliability assessment. While these tools may support patient education, treatment-related responses require caution, and LLMs should be used as adjuncts rather than substitutes for clinical counseling. Full article

Background/Objectives: Healthcare workplace violence has evolved into a global crisis, significantly impacting high-risk specialties. While the “Second Victim Phenomenon” (SVP) is well-established for trauma following medical errors, the specific psychological trauma resulting from intentional external aggression remains conceptually under-defined. This study aims to introduce and define the “Violence Victim Phenomenon” (VVP) by exploring the lived experiences of anesthesiology and intensive care specialists, providing a theoretical framework for this distinct clinical state. Methods: A qualitative study was conducted with ten anesthesiology and intensive care specialists using a semi-structured focus group discussion. The session was subjected to thematic analysis using MAXQDA software. The analysis focused on the nature of violence encountered, psychological and professional impacts, and the role of institutional support systems. Results: The thematic analysis identified six core dimensions of VVP: forms and trajectories of violence, vulnerability amplifiers, psychological and occupational sequelae, coping and containment strategies, expectations of institutional support, and pandemic-specific intensifiers. Participants described a trauma profile comparable to SVP in severity but distinct in its etiology, rooted in intentional harm and “institutional abandonment.” VVP is characterized by a profound sense of vulnerability, loss of professional dignity, and a perceived lack of legal and administrative protection. Conclusions: VVP represents a critical gap in current academic literature. Defining VVP allows for a more nuanced understanding of the trauma healthcare workers face due to intentional aggression. To mitigate VVP, healthcare institutions must move beyond basic security measures toward a “just culture” that provides structured psychological, legal, and managerial support, recognizing clinicians as victims of systemic failure. Full article

Background: Surgeons’ stress can significantly impact performance, leading to medical errors. Various factors contribute to stress, including a procedure’s complexity and surgeon experience. However, the field currently lacks a standardised approach to measuring stress in surgeons of different ages and experience levels. Materials and Methods: This systematic review evaluated heart rate variability (HRV) measures used in surgery to assess stress, considering surgeon age and experience. We searched PubMed, Scopus, and Web of Science following PRISMA guidelines, focusing on studies reporting HRV measurements in surgeons during surgery and comparing different surgeries, procedures, or surgeon experiences. Results: Out of 1821 reviewed studies, nine papers met the criteria, which involved 74 subjects. These studies reported various HRV measures, including heart rate, RMSSD, SDNN, pNN50, LF/HF ratio, LF, and HF. Although all time-domain features tended to show a negative response to stress, frequency-domain measures exhibited consistent patterns. However, these findings should be considered preliminary due to the small number of papers, high heterogeneity among studies, and the fact that no study has established a standard for comparing HRV across different surgeon ages or experience levels. Conclusions: Finally, these findings call for future studies with robust designs to explore the use of HRV parameters for measuring stress over time while considering surgeon age and experience. Full article