Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data.

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Prediction of outcomes for rescue cerclage in cervical insufficiency: A multicenter retrospective study.

Purpose: Cervical insufficiency is a significant risk factor for preterm birth and miscarriage during the second trimester; cervical cerclage is a treatment option. This study seeks to evaluate the predictive roles of various clinical factors and to develop predictive models for immediate and long-term outcomes after rescue cerclage. Methods: We conducted a multicenter retrospective study on patients who underwent rescue cerclage at 14 to 26 weeks of gestation. Data were collected from the Electronic Medical Record systems of participating hospitals. Outcomes were dichotomized into immediate failure (inability to maintain pregnancy for at least 48 hours post-cerclage, gestational latency < 2 days) and long-term success (maintenance of pregnancy until at least 28 weeks of gestation). Clinical factors influencing these outcomes were analyzed. Results: The study included 98 patients. Immediate failure correlated with longer prolapsed membrane lengths, elevated C-reactive protein levels at admission, and extended operation time. The successful maintenance of pregnancy until at least 28 weeks was associated with earlier gestational age at diagnosis, negative AmniSure test results, longer lengths of the functional cervix, and smaller cervical dilatation at the time of cerclage. Binary logistic regression models for immediate failure and long-term success exhibited excellent and good predictive abilities, respectively (AUROC = 0.912, 95% CI: 0.834-0.989; and AUROC = 0.872, 95% CI: 0.788-0.956). Conclusion: The developed logistic regression models offer a valuable tool for the prognostic assessment of patients undergoing rescue cerclage, enabling informed clinical decision-making.

Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data.

Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.

Prognostic factors and predictive models for patients with lung large cell neuroendocrine carcinoma: Based on SEER database.

BACKGROUND: Lung Large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive, high-grade neuroendocrine carcinoma with a poor prognosis, mainly seen in elderly men. To date, we have found no studies on predictive models for LCNEC. METHODS: We extracted data from the Surveillance, Epidemiology, and End Results (SEER) database of confirmed LCNEC from 2010 to 2018. Univariate and multivariate Cox proportional risk regression analyses were used to identify independent risk factors, and then we constructed a novel nomogram and assessed the predictive effectiveness by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: A total of 2546 patients with LCNEC were included, excluding those diagnosed with autopsy or death certificate, tumor, lymph node, metastasis (TNM) stage, tumor grade deficiency, etc., and finally, a total of 743 cases were included in the study. After univariate and multivariate analyses, we concluded that the independent risk factors were N stage, intrapulmonary metastasis, bone metastasis, brain metastasis, and surgical intervention. The results of ROC curves, calibration curves, and DCA in the training and validation groups confirmed that the nomogram could accurately predict the prognosis. CONCLUSIONS: The nomogram obtained from our study is expected to be a useful tool for personalized prognostic prediction of LCNEC patients, which may help in clinical decision-making.

Integrating Patient Preferences with Guideline-Based Care in Neurogenic Lower Urinary Tract Dysfunction After Spinal Cord Injury.

Individual and social factors are important for clinical decision-making in patients with neurogenic bladder secondary to spinal cord injury (SCI). These factors include the availability of caregivers, social infrastructure, and personal preferences, which all can drive bladder management decisions. These elements can be overlooked in clinical decision-making; therefore, there is a need to elicit and prioritize patient preferences and values into neurogenic bladder care to facilitate personalized bladder management choices. For the purposes of this article, we review the role of guideline-based care and shared decision-making in the SCI population with neurogenic lower urinary tract dysfunction.

Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review.

After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.

Medicine Package Recommendation via Dual-Level Interaction Aware Heterogeneous Graph.

Medicine package recommendation aims to assist doctors in clinical decision-making by recommending appropriate packages of medicines for patients. Current methods model this task as a multi-label classification or sequence generation problem, focusing on learning relationships between individual medicines and other medical entities. However, these approaches uniformly overlook the interactions between medicine packages and other medical entities, potentially resulting in a lack of completeness in recommended medicine packages. Furthermore, medicine commonsense knowledge considered by current methods is notably limited, making it challenging to delve into the decision-making processes of doctors. To solve these problems, we propose DIAGNN, a Dual-level Interaction Aware heterogeneous Graph Neural Network for medicine package recommendation. Specifically, DIAGNN explicitly models interactions of medical entities within electronic health records(EHRs) at two levels, individual medicine and medicine package, leveraging a heterogeneous graph. A dual-level interaction aware graph convolutional network is utilized to capture semantic information in the medical heterogeneous graph. Additionally, we incorporate medication indications into the medical heterogeneous graph as medicine commonsense knowledge. Extensive experimental results on real-world datasets validate the effectiveness of the proposed method.

Modelo de riesgo de mortalidad a largo plazo en pacientes con infección atendidos por servicios de emergencias prehospitalarios / Long-term risk of death in patients with infection attended by prehospital emergency services

Objetivo: Diseñar y validar un modelo de riesgo con variables determinadas a nivel prehospitalario para predecir el riesgo de mortalidad a largo plazo (1 año) en pacientes con infección. Métodos: Estudio multicéntrico, observacional prospectivo, sin intervención, en pacientes adultos con sospecha infección atendidos por unidades de soporte vital avanzado y trasladados a 4 hospitales españoles entre el 1 de junio de 2020 y el 30 de junio de 2022. Se recogieron variables demográficas, fisiológicas, clínicas y analíticas. Se construyó y validó un modelo de riesgo para la mortalidad a un año usando una regresión de Cox.Resultados: Se incluyeron 410 pacientes, con una tasa de mortalidad acumulada al año del 49%. La tasa de diagnóstico de sepsis (infección e incremento sobre el SOFA basal $ 2 puntos) fue del 29,2% en supervivientes frente a un 56,7% en no supervivientes. El modelo predictivo obtuvo un área bajo la curva de la característica operativa del receptor para la mortalidad a un año fue de 0,89, e incluyó: edad, institucionalización, índice de comorbilidad de Charlson ajustado por edad, presión parcial de dióxido de carbono, potasio, lactato, nitrógeno ureico en sangre, creatinina, saturación en relación con fracción inspirada de oxígeno y diagnóstico de sepsis.Conclusiones: El modelo desarrollado con variables epidemiológicas, analíticas y clínicas mostró una excelente capacidad predictiva, y permitió identificar desde el primer contacto del paciente con el sistema sanitario, a modo de evento centinela, casos de alto riesgo.(AU)

Objectives: To develop and validate a risk model for 1-year mortality based on variables available from earlyprehospital emergency attendance of patients with infection. Methods: Prospective, observational, noninterventional multicenter study in adults with suspected infection transferred to 4 Spanish hospitals by advanced life-support ambulances from June 1, 2020, through June 30, 2022. We collected demographic, physiological, clinical, and analytical data. Cox regression analysis was used to develop and validate a risk model for 1-year mortality. Results: Four hundred ten patients were enrolled (development cohort, 287; validation cohort, 123). Cumulative mortality was 49% overall. Sepsis (infection plus a Sepsis-related Organ Failure Assessment score of 2 or higher) was diagnosed in 29.2% of survivors vs 56.7% of nonsurvivors. The risk model achieved an area under the receiver operating characteristic curve of 0.89 for 1-year mortality. The following predictors were included in the model: age; institutionalization; age-adjusted Charlson comorbidity index; PaCO2; potassium, lactate, urea nitrogen, and creatinine levels; fraction of inspired oxygen; and diagnosed sepsis. Conclusions: The model showed excellent ability to predict 1-year mortality based on epidemiological, analytical, andclinical variables, identifying patients at high risk of death soon after their first contact with the health care system.(AU)

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Clinical decision making when cytology indicates a Warthin tumor.

Warthin tumor (WT) is a benign tumor usually affecting the parotid gland. The main diagnostic tool remains ultrasound combined with fine-needle aspiration cytology (FNAC). This study aims to examine how reliably FNAC indicates WT for clinical decision making regarding surgical versus conservative management. We included all patients who underwent FNAC from a parotid gland lesion between 2016 and 2018 at our institution, and whose FNAC revealed WT suspicion. The FNACs were divided into three groups based on the cytology report: certain, likely, and possible WT. The patients were divided into two groups based on having had either surgery or follow-up. We sent a questionnaire to patients who had not undergone surgery in order to obtain follow-up for a minimum of four years. Altogether, 135 FNAC samples, from 133 tumors and 125 patients, showed signs of WT. Of the 125 patients, 44 (35%) underwent surgery, and 81 (65%) were managed conservatively. Preoperative misdiagnosis in FNAC occurred in three (7%) surgically treated tumors. Their FNACs were reported as possible WTs, but histopathology revealed another benign lesion. In the conservatively treated group, two patients underwent surgery later during the follow-up. Cytological statements of WT were seldom false, and none were malignant. The majority of the patients were only followed-up and rarely required further treatment. A certain or likely diagnosis of WT in the FNAC report by an experienced head and neck pathologist is highly reliable in selecting patients for conservative surveillance.

Decision discovery using clinical decision support system decision log data for supporting the nurse decision-making process.

BACKGROUND: Decision-making in healthcare is increasingly complex; notably in hospital environments where the information density is high, e.g., emergency departments, oncology departments, and psychiatry departments. This study aims to discover decisions from logged data to improve the decision-making process. METHODS: The Design Science Research Methodology (DSRM) was chosen to design an artifact (algorithm) for the discovery and visualization of decisions. The DSRM's different activities are explained, from the definition of the problem to the evaluation of the artifact. During the design and development activities, the algorithm itself is created. During the demonstration and evaluation activities, the algorithm was tested with an authentic synthetic dataset. RESULTS: The results show the design and simulation of an algorithm for the discovery and visualization of decisions. A fuzzy classifier algorithm was adapted for (1) discovering decisions from a decision log and (2) visualizing the decisions using the Decision Model and Notation standard. CONCLUSIONS: In this paper, we show that decisions can be discovered from a decision log and visualized for the improvement of the decision-making process of healthcare professionals or to support the periodic evaluation of protocols and guidelines.

Causal machine learning for predicting treatment outcomes.

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.

De-imFAR phase II project: a study protocol for a cluster randomised implementation trial to evaluate the effectiveness of de-implementation strategies to reduce low-value statin prescribing in the primary prevention of cardiovascular disease.

INTRODUCTION: This study aims to reduce potentially inappropriate prescribing (PIP) of statins and foster healthy lifestyle promotion in cardiovascular disease (CVD) primary prevention in low-risk patients. To this end, we will compare the effectiveness and feasibility of several de-implementation strategies developed following the structured design process of the Behaviour Change Wheel targeting key determinants of the clinical decision-making process in CVD prevention. METHODS AND ANALYSIS: A cluster randomised implementation trial, with an additional control group, will be launched, involving family physicians (FPs) from 13 Integrated Healthcare Organisations (IHOs) of Osakidetza-Basque Health Service with non-zero incidence rates of PIP of statins in 2021. All FPs will be exposed to a non-reflective decision assistance strategy based on reminders and decision support tools. Additionally, FPs from two of the IHOs will be randomly assigned to one of two increasingly intensive de-implementation strategies: adding a decision information strategy based on knowledge dissemination and a reflective decision structure strategy through audit/feedback. The target population comprises women aged 45-74 years and men aged 40-74 years with moderately elevated cholesterol levels but no diagnosed CVD and low cardiovascular risk (REGICOR<7.5%), who attend at least one appointment with any of the participating FPs (May 2022-May 2023), and will be followed until May 2024. We use the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework to evaluate outcomes. The main outcome will be the change in the incidence rate of PIP of statins and healthy lifestyle counselling in the study population 12 and 24 months after FPs' exposure to the strategies. Moreover, FPs' perception of their feasibility and acceptability, and patient experience regarding the quality of care received will be evaluated. ETHICS AND DISSEMINATION: The study was approved by the Basque Country Clinical Research Ethics Committee and was registered in ClinicalTrials.gov (NCT04022850). Results will be disseminated in scientific peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04022850.

Reflective Clinical Judgment Questions to Educate the Next Generation of Nurses.

BACKGROUND: The complexity of health care requires entry-level nurses to have competent clinical judgment skills. In response, a nursing program created Reflective Clinical Judgment Questions (RCJQ) to guide students in the development of clinical judgment. METHOD: The RCJQ incorporates the Clinical Judgment Measurement Model, the National Council of State Boards of Nursing's action questions, and the American Association of Colleges of Nursing's core competencies for professional nursing education. The RCJQ includes cognitive process questions and self-reflection questions aligned to the prelicensure subcompetencies to direct student thinking and build a routine for clinical decision making. RESULTS: The RCJQ provides faculty with a framework to teach clinical judgment and incorporates self-reflective questions to guide decision making for safe and effective client care. CONCLUSION: The RCJQ streamlines the clinical judgment process and guides students to achieve essential outcomes in classroom, clinical, and simulation settings to prepare for clinical practice. [J Nurs Educ. 2024;63(3):182-185.].

Generative AI in healthcare: an implementation science informed translational path on application, integration and governance.

BACKGROUND: Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medico-legal implications, integration into healthcare service delivery and workforce utilisation. Also, there is not a clear pathway to implement and integrate generative AI in healthcare delivery. METHODS: This article aims to provide a comprehensive overview of the use of generative AI in healthcare, focusing on the utility of the technology in healthcare and its translational application highlighting the need for careful planning, execution and management of expectations in adopting generative AI in clinical medicine. Key considerations include factors such as data privacy, security and the irreplaceable role of clinicians' expertise. Frameworks like the technology acceptance model (TAM) and the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) model are considered to promote responsible integration. These frameworks allow anticipating and proactively addressing barriers to adoption, facilitating stakeholder participation and responsibly transitioning care systems to harness generative AI's potential. RESULTS: Generative AI has the potential to transform healthcare through automated systems, enhanced clinical decision-making and democratization of expertise with diagnostic support tools providing timely, personalized suggestions. Generative AI applications across billing, diagnosis, treatment and research can also make healthcare delivery more efficient, equitable and effective. However, integration of generative AI necessitates meticulous change management and risk mitigation strategies. Technological capabilities alone cannot shift complex care ecosystems overnight; rather, structured adoption programs grounded in implementation science are imperative. CONCLUSIONS: It is strongly argued in this article that generative AI can usher in tremendous healthcare progress, if introduced responsibly. Strategic adoption based on implementation science, incremental deployment and balanced messaging around opportunities versus limitations helps promote safe, ethical generative AI integration. Extensive real-world piloting and iteration aligned to clinical priorities should drive development. With conscientious governance centred on human wellbeing over technological novelty, generative AI can enhance accessibility, affordability and quality of care. As these models continue advancing rapidly, ongoing reassessment and transparent communication around their strengths and weaknesses remain vital to restoring trust, realizing positive potential and, most importantly, improving patient outcomes.

Construction and validation of machine learning models for predicting distant metastases in newly diagnosed colorectal cancer patients: A large-scale and real-world cohort study.

BACKGROUND: More accurate prediction of distant metastases (DM) in patients with colorectal cancer (CRC) would optimize individualized treatment and follow-up strategies. Multiple prediction models based on machine learning have been developed to assess the likelihood of developing DM. METHODS: Clinicopathological features of patients with CRC were obtained from the National Cancer Center (NCC, China) and the Surveillance, Epidemiology, and End Results (SEER) database. The algorithms used to create the prediction models included random forest (RF), logistic regression, extreme gradient boosting, deep neural networks, and the K-Nearest Neighbor machine. The prediction models' performances were evaluated using receiver operating characteristic (ROC) curves. RESULTS: In total, 200,958 patients, 3241 from NCC and 197,717 CRC from SEER were identified, of whom 21,736 (10.8%) developed DM. The machine-learning-based prediction models for DM were constructed with 12 features remaining after iterative filtering. The RF model performed the best, with areas under the ROC curve of 0.843, 0.793, and 0.806, respectively, on the training, test, and external validation sets. For the risk stratification analysis, the patients were separated into high-, middle-, and low-risk groups according to their risk scores. Patients in the high-risk group had the highest incidence of DM and the worst prognosis. Surgery, chemotherapy, and radiotherapy could significantly improve the prognosis of the high-risk and middle-risk groups, whereas the low-risk group only benefited from surgery and chemotherapy. CONCLUSION: The RF-based model accurately predicted the likelihood of DM and identified patients with CRC in the high-risk group, providing guidance for personalized clinical decision-making.

Knowledge, perceptions, attitudes, and barriers pertaining to genetic literacy among surgeons: a scoping review.

BACKGROUND: The rapid evolution of genetic technologies and utilization of genetic information for clinical decision-making has necessitated increased surgeon participation in genetic counselling, testing, and appropriate referral of patients for genetic services, without formal training in genetics. We performed a scoping review to describe surgeons' knowledge, perceptions, attitudes, and barriers pertaining to genetic literacy in the management of patients who had confirmed cancer or who were potentially genetically at risk. METHODS: We conducted a scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews checklist. We performed a comprehensive literature search, and 2 reviewers independently screened studies for inclusion. These studies included surgeons involved in the care of patients with confirmed gastrointestinal, breast, and endocrine and neuroendocrine cancers, or patients who were potentially genetically at risk for these cancers. RESULTS: We analyzed 17 studies, all of which used survey or interview-based formats. Many surgeons engaged in genetic counselling, testing, and referral, but reported low confidence and comfort in doing so. Knowledge assessments showed lower confidence in identifying genetic inheritance patterns and hereditary cancer syndromes, but awareness was higher among surgeons with greater clinical volume or subspecialty training in oncology. Surgeons felt responsible for facilitating these services and explicitly requested educational support in genetics. Barriers to genetic literacy were identified and catalogued at patient, surgeon, and system levels. CONCLUSION: Surgeons frequently engage in genetics-related tasks despite a lack of formal genetics training, and often report low knowledge, comfort, and confidence in providing such services. We have identified several barriers to genetic literacy that can be used to develop interventions to enhance genetic literacy among surgeons.

Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.

The effect of case-based teaching method on professional competence and clinical decision-making levels of public health nursing students: A sequential explanatory mixed-methods study.

OBJECTIVES: To evaluate the effect of case-based teaching method applied to fourth year nursing students on their professional competence and clinical decision-making levels. DESIGN: A quasi-experimental design study with a sequential-exploratory mixed-method approach. SAMPLE: 64 nursing students enrolled in the Public Health Nursing course. METHODS: A case-based teaching program was applied to the students that cover the topics of the Public Health Nursing course. Quantitative phase data were collected with the Clinical Decision Making in Nursing Scale and Nursing Students' Competence Scale. For the qualitative part, focus group interviews were conducted with a Structured Interview Form. RESULTS: It was determined that the total and subscale posttest scores of the students increased significantly compared to their pretest scores (p < .001). A moderate positive correlation was found between the total scores received from the scale and a significant positive correlation was found between researching information and adopting new information impartially and all sub-dimensions except care (p < .05). Three main themes emerged from the focus group interviews conducted after the case-based teaching method experience: usefulness, limitations, and improvement. CONCLUSIONS: Case-based teaching method is effective on students' professional competence and clinical decision-making scores. Students' professional competence levels positively affect their clinical decision-making levels.

Impact of muscle biopsy on the clinical decision-making process in patients with suspected idiopathic inflammatory myopathy.

BACKGROUND: The significance of muscle biopsy as a diagnostic tool in idiopathic inflammatory myopathies (IIM) remains elusive. We aimed to determine the diagnostic weight that has been given to muscle biopsy in patients with suspected IIM, particularly in terms of clinical diagnosis and therapeutic decisions. MATERIAL AND METHODS: In this retrospective multicentric study, we analyzed muscle biopsy results of adult patients with suspected IIM referred to a tertiary center between January 1, 2007, and October 31, 2021. Information regarding referral department, suspected diagnosis, biopsy site, demographic, clinical, laboratory data, and imaging results were extracted. Statistical analyses included the level of agreement between suspected and histological diagnosis and calculation of diagnostic performance (positive and negative predictive values, positive and negative likelihood ratios, sensitivity, and specificity of muscle biopsy in relation to clinical diagnosis and/or treatment initiation). Performance was tested in different strata based on clinical pre-test probability. RESULTS: Among 758 muscle biopsies, IIM was histologically compatible in 357/758 (47.1%) cases. Proportion of IIM was higher if there was a solid clinical pre-test probability (64.3% vs. 42.4% vs. 48% for high, medium and low pre-test probability). Sensitivity and specificity of muscle biopsy were highest (82%) when the diagnosis by the clinician was used as outcome scenario. Negative predictive value was only moderate (between 63% and 80%) and lowest if autoantibodies were positive (35%). CONCLUSION: In patients with clinically suspected IIM, approximately 50% of biopsies revealed features indicative of IIM. Diagnostic performance of muscle biopsy was moderate to high depending on clinical pre-test probability.