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Background Understanding how clinical learning takes place and what could stand as an indicator of clinical learning is crucial. There are existing challenges in the clinical learning environment that require clinical indicators. These serve as accountability standards in settings that have challenges of human resources and material poverty. Thus, clinical indicators are pre-requisites for self-regulation and self-directedness to promote lifelong learning. The reality that exists in today’s Malawian health education institutions and clinical settings requires that those in training receive support and guidance on how essential competencies and skills can be acquired during training. Objectives The objective of this scoping review was to identify current literature on clinical learning indicators among health professional students. Method The Joanna Briggs Institute’s (May 2020) standards for scoping reviews including narrative synthesis were followed in the conduct of this review. The protocol was registered in the Open Science Framework https://osf.io/yj9nr. Results The results generated seven themes on clinical learning process and these are (1) planning for learning, (2) awareness of self-directedness in clinical learning, (3) knowledge of achievement of learning outcomes, (4) educators’ evidence of students’ clinical learning, (5) students’ perspective on clinical learning, (6) students’ knowledge of achievement in practice and (7) impact of prior knowledge on clinical learning. Conclusion Clinical learning indicators among undergraduate health professionals are essential and clinical learning should be a planned endeavour by the students before the clinical placement settings. Contribution This study contributed to understanding clinical learning indicators and self-regulated learning practices among healthcare students.

Delayed recall of words in a verbal learning test is a sensitive measure for the diagnosis of amnestic mild cognitive impairment (aMCI) and early Alzheimer's disease (AD). The relative validity of different retention intervals of delayed recall has not been well characterized. Using the Auditory Verbal Learning Test-Huashan version, we compared the differentiating value of short-term delayed recall (AVL-SR, that is, a 3- to 5-minute delay time) and long-term delayed recall (AVL-LR, that is, a 20-minute delay time) in distinguishing patients with aMCI (n = 897) and mild AD (n = 530) from the healthy elderly (n = 1215). In patients with aMCI, the correlation between AVL-SR and AVL-LR was very high (r = 0.94), and the difference between the two indicators was less than 0.5 points. There was no difference between AVL-SR and AVL-LR in the frequency of zero scores. In the receiver operating characteristic curves analysis, although the area under the curve (AUC) of AVL-SR and AVL-LR for diagnosing aMCI was significantly different, the cut-off scores of the two indicators were identical. In the subgroup of ages 80 to 89, the AUC of the two indicators showed no significant difference. Therefore, we concluded that AVL-SR could substitute for AVL-LR in identifying aMCI, especially for the oldest patients.

Around 30% of individuals with schizophrenia remain symptomatic and significantly impaired despite antipsychotic treatment and are considered to be treatment resistant. Clinicians are currently unable to predict which patients are at higher risk of treatment resistance. To determine whether genetic liability for schizophrenia and/or clinical characteristics measurable at illness onset can prospectively indicate a higher risk of treatment-resistant psychosis (TRP). In 1070 individuals with schizophrenia or related psychotic disorders, schizophrenia polygenic risk scores (PRS) and large copy number variations (CNVs) were assessed for enrichment in TRP. Regression and machine-learning approaches were used to investigate the association of phenotypes related to demographics, family history, premorbid factors and illness onset with TRP. Younger age at onset (odds ratio 0.94, P = 7.79 × 10-13) and poor premorbid social adjustment (odds ratio 1.64, P = 2.41 × 10-4) increased risk of TRP in univariate regression analyses. These factors remained associated in multivariate regression analyses, which also found lower premorbid IQ (odds ratio 0.98, P = 7.76 × 10-3), younger father's age at birth (odds ratio 0.97, P = 0.015) and cannabis use (odds ratio 1.60, P = 0.025) increased the risk of TRP. Machine-learning approaches found age at onset to be the most important predictor and also identified premorbid IQ and poor social adjustment as predictors of TRP, mirroring findings from regression analyses. Genetic liability for schizophrenia was not associated with TRP. People with an earlier age at onset of psychosis and poor premorbid functioning are more likely to be treatment resistant. The genetic architecture of susceptibility to schizophrenia may be distinct from that of treatment outcomes.

Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only \"real world\" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given \"confidence threshold\". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how \"real world\" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.

Background Iron deficiency (ID) limits the neurodevelopmental potential of more than 200 million children each year. Iron therapy started when IDA is first diagnosed—typically by screening for anemia or detection of clinical symptoms of IDA at 12 months of age—does not fully correct earlier ID-mediated brain dysfunction, underscoring the need for low-cost, easily implementable adjunct therapies to iron to treat or prevent this dysfunction in high-risk populations. Supplementation with the essential nutrient choline lessens damage done to the developing hippocampus when given with iron in pre-clinical rodent models, and choline supplementation improves hippocampus-mediated memory and learning in 2–3-year-old children with Fetal Alcohol Spectrum Disorders, a condition associated with hippocampal damage and one for which ID is a component of the neuropathology. Choline has not been tested in children with IDA. Our overall aim is to conduct a randomized, placebo-controlled clinical trial to test whether nine months of daily choline supplementation along with standard iron therapy improves hippocampus-dependent neurobehavioral outcomes in Ugandan infants with IDA. Methods Three hundred 6-month-old infants with IDA who present to immunization clinics at Mulago and Kawempe National Referral Hospitals in Kampala, Uganda, will be randomized to iron plus choline or iron plus placebo. Iron (oral ferrous sulfate 2 mg/kg/day) will be given for the first 3 months of follow-up, and a dispersible tablet of choline (200 mg as choline bitartrate) or identical placebo will be given daily for all 9 months of follow-up. We will conduct neurobehavioral tests assessing hippocampus-specific memory and attention and global cognition at enrollment (when each infant is 6 months of age) and after 9 months of follow-up (when each infant is 15 months of age). Discussion If we find a neurobehavioral benefit when choline is given along with iron, choline could be added immediately to standard of care treatment for IDA. This low-cost intervention could safely mitigate the brain dysfunction of early-life ID that is often not diagnosed until the hippocampal critical window is closing, providing life-long benefit for both the individual and the economic and social prosperity of entire regions. Trial registration Clinical trials.gov NCT06527391. Registered on 24 July 2024.

Background Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text. Methods A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit. Results The random forests model classified sentences as “good” or “bad” with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p  < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care. Conclusions We have developed a web-based summarization and visualization tool, CERC ( https://newton.isye.gatech.edu/CERC1/ ), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.

Hospitals routinely collect large amounts of administrative data such as length of stay, 28-day readmissions, and hospital-acquired complications; yet, these data are underused for continuing professional development (CPD). First, these clinical indicators are rarely reviewed outside of existing quality and safety reporting. Second, many medical specialists view their CPD requirements as time-consuming, having minimal impact on practice change and improving patient outcomes. There is an opportunity to build new user interfaces based on these data, designed to support individual and group reflection. Data-informed reflective practice has the potential to generate new insights about performance, bridging the gap between CPD and clinical practice. This study aims to understand why routinely collected administrative data have not yet become widely used to support reflective practice and lifelong learning. We conducted semistructured interviews (N=19) with thought leaders from a range of backgrounds, including clinicians, surgeons, chief medical officers, information and communications technology professionals, informaticians, researchers, and leaders from related industries. Interviews were thematically analyzed by 2 independent coders. Respondents identified visibility of outcomes, peer comparison, group reflective discussions, and practice change as potential benefits. The key barriers included legacy technology, distrust with data quality, privacy, data misinterpretation, and team culture. Respondents suggested recruiting local champions for co-design, presenting data for understanding rather than information, coaching by specialty group leaders, and timely reflection linked to CPD as enablers to successful implementation. Overall, there was consensus among thought leaders, bringing together insights from diverse backgrounds and medical jurisdictions. We found that clinicians are interested in repurposing administrative data for professional development despite concerns with underlying data quality, privacy, legacy technology, and visual presentation. They prefer group reflection led by supportive specialty group leaders, rather than individual reflection. Our findings provide novel insights into the specific benefits, barriers, and benefits of potential reflective practice interfaces based on these data sets. They can inform the design of new models of in-hospital reflection linked to the annual CPD planning-recording-reflection cycle.

Abstract Issue The National Cardiovascular Plan (NCP) of the Czech Republic for 2025-2035 is a document with the vision to ensure that every citizen of the Czech Republic has the opportunity to prevent cardiovascular disease and to ensure the highest possible quality of care and life regardless of geographical location or stage of the disease. One of its strategic objectives and needs is the availability of epidemiological data and analyses of indicators of the quality of care provided, i.e. establishment of the National Cardiology Information System (NCIS). Description of the problem The availability of data on cardiovascular disease in the Czech Republic is crucial for the implementation of the NCP. The NCIS, developed by the Czech Society of Cardiology and the Institute of Health Information and Statistics of the Czech Republic, integrates data from different sources of the NHIS. Results Through the data linkage of data on diagnoses, healthcare use, social services, mortality, socio-economic indicators and other data available in the National Health Information System and the National Social Information System, the NCIS covers key evaluation dimensions and segments of cardiology care: (i) epidemiology of cardiovascular diseases, characteristics of patients and their risk factors, (ii) primary and secondary prevention of cardiovascular diseases, (iii) treatment burden of providers, (iv) acute inpatient care with an emphasis on specialised and highly specialised care, (v) care of paediatric patients with cardiovascular disease, (vi) end-of-life care of patients with cardiovascular disease, (vii) indicators of availability, capacities, outcomes and quality of care. Lessons Linking health and social data from multiple sources is an effective way of developing new information systems focused on complex area of medicine and public health. The Czech experience shows that even complex, multi-sectoral datasets can be used practically for system-level management and prediction. Key messages • The data linkage of already existing databases and the secondary usage of these data is a key approach for obtaining complex information with minimalised administrative burden. • The integrated system enables various types of analyses from simple descriptive studies to analysis of survival of patients, analysis of risk factors, predictive modelling, regional disparities etc

The global rise of metabolic associated fatty liver disease reflects the urgent need for accurate, noninvasive diagnostic approaches. The invasive nature of liver biopsy and the limited sensitivity of ultrasound in detecting early steatosis highlight a critical diagnostic gap. Artificial intelligence (AI) has emerged as a transformative tool, enabling the automated detection and grading of hepatic steatosis (HS) from medical imaging data. This review aims to quantitatively evaluate the diagnostic performance of AI models for HS, explore sources of interstudy heterogeneity, and provide an appraisal of their clinical applicability, translational potential, and the major barriers impeding widespread implementation. PubMed, Cochrane Library, Embase, Web of Science, and IEEE Xplore databases were searched until September 24, 2025. Studies using AI for HS diagnosis, meeting predefined PIRT (Patient Selection, Index Test, Reference Standard, Flow and Timing) framework and providing extractable data were included. Diagnostic performance indicators, including sensitivity, specificity, and the area under the summary receiver operating characteristic curve (AUC), were extracted and quantitatively synthesized. Meta-analyses were conducted using a bivariate random effects model. The methodological quality and risk of bias were evaluated using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool. Heterogeneity was assessed through the I² statistic, bivariate box plots, 95% PIs, and threshold effect analysis. Clinical applicability was examined using the Fagan nomogram and likelihood ratio tests. A total of 36 eligible studies were identified, of which 33 (comprising 36 cohorts) were included in the subgroup analyses. Results demonstrated excellent diagnostic accuracy of AI models, with a summary sensitivity of 0.95 (95% CI 0.93-0.96), specificity of 0.93 (95% CI 0.91-0.94), and an AUC of 0.98 (95% CI 0.96-0.99). Clinical applicability analysis (positive likelihood ratio >10; negative likelihood ratio <0.1) supported AI's strong potential for both confirming and excluding HS. However, substantial heterogeneity was observed across studies (I² >75%). According to QUADAS-2, a high risk of bias, particularly in the Patient Selection domain (44.4%), may have contributed to the overestimation of real-world performance. Subgroup analyses showed that deep learning models significantly outperformed traditional machine learning approaches (AUC: 0.98 vs 0.94). Models using ultrasound or histopathology references, retrospective designs, transfer learning, and public datasets achieved the highest accuracy (AUC 0.98-0.99) but contributed to interstudy heterogeneity. AI demonstrates remarkable potential for noninvasive screening and assessment of HS, especially in primary care. Nonetheless, clinical translation remains limited by performance variability, retrospective designs, lack of external validation, practical barriers such as data privacy and workflow integration. Future studies should prioritize prospective multicenter trials and standardized external validation to bridge the gap between current evidence and clinical application. The key innovation of this review lies in establishing a unified, modality-agnostic analytical framework that integrates evidence beyond single-modality evaluations.

The aim of this study was to evaluate the potential benefit of blood inflammation in the diagnosis of non-small cell lung cancer (NSCLC) and propose a machine-learning-based method to predict NSCLC in asymptomatic adults. A cross-sectional study was evaluated using medical records of 139 patients with non-small cell lung cancer and physical examination data from May 2022 to May 2023 of 198 healthy controls. The NSCLC cohort comprised 128 cases of adenocarcinoma, 3 cases of squamous cell carcinoma, and 8 cases of other NSCLC subtypes. The correlation between inflammatory and nutritional markers, such as monocytes, neutrophils, LMR, NLR, PLR, PHR and non-small cell lung cancer was examined. Features were selected using Python's feature selection library and analyzed by five algorithms. The predictive ability of the model for non-small cell lung cancer diagnosis was assessed by precision, accuracy, recall, F1 score, and area under the curve (AUC). The results showed that the top 14 important factors were PDW, age, TP, RBC, HGB, LYM, LYM%, RDW, PLR, LMR, PHR, MONO, MONO%, gender. Additionally, the naive Bayes (NB) algorithm demonstrated the highest overall performance in predicting adult NSCLC among the five machine learning algorithms, achieving an accuracy of 0.87, a macro average F1 score of 0.85, a weighted average F1 score of 0.87, and an AUC of 0.84. In feature ranking, platelet distribution width was the most important feature, and the NB algorithm performed best in predicting adult NSCLC diagnosis.