Clinical Decision-making Ability Research Articles

Understanding the factors that influence CT utilization for mild traumatic brain injury in a low resource setting - a qualitative study using the Theoretical Domains Framework

IntroductionIn low resource settings (LRS), utilization of Computed Tomography scan (CTS) for mild traumatic brain injuries (mTBIs) presents unique challenges and considerations given the limited infrastructure, financial resources, and trained personnel. The Theoretical Domains Framework (TDF) offers a comprehensive theoretical lens to explore factors influencing the decision-making to order CTS for mTBI by imaging referrers (IRs). ObjectivesThe primary objective was to explore IRs’ beliefs about factors influencing CT utilization in mTBIs using TDF in Uganda.Differences in the factors influencing CTS ordering behavior across specialties, levels of experience, and hospital category were also explored. Materials and MethodsIn-depth semi-structured interviews guided by TDF were conducted among purposively selected IRs from 6 tertiary public and private hospitals with functional CTS services. A thematic analysis was performed with codes and emerging themes developed based on the TDF. ResultsEleven IRs including medical officers, non-neurosurgeon specialists and neurosurgeons aged on average 42 years (SD+/-12.3 years) participated.Identified factors within skills domain involved IRs’ clinical assessment and decision-making abilities, while beliefs about capabilities and consequences encompassed their confidence in diagnostic abilities and perceptions of CTS risks and benefits. The environmental context and resources domain addressed the availability of CT scanners and financial constraints. The knowledge domain elicited IRs’ understanding of clinical guidelines and evidence-based practices while social influences considered peer influence and institutional culture. For memory, attention & decision processes domain, IRs adherence to guidelines and intentions to order CT scans were cited. ConclusionUsing TDF, IRs identified several factors believed to influence decision making to order CTS in mTBI in a LRS. The findings can inform stakeholders to develop targeted strategies and evidence-based interventions to optimize CT utilization in mTBI such as; educational programs, workflow modifications, decision support tools, and infrastructure improvements, among others.

The effect of nomophobic behaviors among nurses on their clinical decision-making perceptions

AimThis study aimed to explore the impact of nomophobic behaviors among hospital nurses on their clinical decision-making perceptions. This understanding can offer insights to enhance the work environment, improve the clinical decision-making ability of nurses and guide medical institutions in the management of related equipment and policy development. BackgroundThe term "nomophobia" refers to the anxiety and fear individuals experience when they cannot use their smartphones or when smartphones are not accessible. Nursing clinical decision-making is a complex process, including a meticulous assessment of the patient's pathological condition and medical history, alongside the application of nursing knowledge and experiential learning rooted in critical thinking. The concept of clinical decision-making perceptions is defined as a deliberate cognitive understanding of one's decision-making processes, which significantly influences the clinical decision-making capabilities of healthcare professionals, thereby impacting the quality of patient care. The factors influencing these clinical decision-making perceptions have been the subject of extensive research. However, there is no Chinese research on the impact of nurses ' nomophobic behaviors on their clinical decision-making perception. DesignA cross-sectional descriptive survey using online-based delivery modes was used. MethodsA descriptive cross-sectional survey design was employed. Using convenience sampling, we surveyed the nurses from a tertiary hospital in Nanjing in May 2023. Data were gathered using a sociodemographic data form, the Nomophobia Questionnaire and the Clinical Decision-Making in Nursing Scale. Techniques including the independent sample t-test, one-way ANOVA, Pearson correlation analysis and linear regression analysis were employed to probe the degree of nomophobia and its effects on their perception of clinical decision-making. Out of 284 questionnaires gathered, 272 were deemed valid, resulting in a 95.8% effective response rate. ResultsThe data revealed that participants exhibited a medium level of nomophobia (54.01 ± 24.09) and clinical decision-making perceptions (144.94 ± 20.08). A robust negative correlation was discerned between nomophobia and clinical decision-making perceptions (r: −0.365, P<0.001). This study highlighted that as the degree of nomophobia intensified, nurses' clinical decision-making perceptions decreased with the increase in nomophobia. ConclusionNomophobic behaviors can hamper nurses' perception of clinical decision-making, potentially leading to inaccuracies or errors. Nurses must use mobile phones judiciously, practice self-regulation and mitigate the disruptive effects of nomophobia on their decision-making. In addition, medical institutions should foster relevant education or craft policies to regularize mobile phone use, augmenting nurses' efficiency and decision-making prowess, enhancing patient care quality, diminishing medical errors and ensuring patient health and safety.

Influences of Nursing Professionalism, Empathy, and Clinical Decision-Making Ability on Shared Decision-Making Awareness among Hemodialysis Nurses

Aim. To examine the relationships among nursing professionalism, empathy, and clinical decision-making ability and the factors influencing shared decision-making awareness in hemodialysis nurses. Background. Self-management and treatment for hemodialysis patients are essential for maintaining health and life in daily life. In this process, shared decision-making in which patients, nurses, and medical teams participate and make decisions together has a greater impact on the health recovery and improvement of quality of life for hemodialysis patients than for any other chronic disease patients. Methods. A cross-sectional descriptive design was employed. Participants were 145 nurses working in the hemodialysis centers at hospitals in Seoul and Gyeonggi-do. Measures included the general characteristics of study participants, nursing professionalism, empathy, clinical decision-making ability, and shared decision-making awareness. Data were collected from May to July, 2022, and multiple linear regression analysis was used to examine the predictive factors of shared decision-making awareness. Results. The strongest predictor was empathy, followed by clinical decision-making ability and the level of education. The explanatory power of the final regression model was 23%. Conclusions. Empathy towards hemodialysis patients was an important factor influencing the shared decision-making awareness in hemodialysis nurses. Implications for Nursing Management. In nursing management, nursing managers or nurses should pay attention to influencing factors to improve the shared decision-making awareness of hemodialysis nurses. Empathy towards hemodialysis patients need to be reinforced to improve the shared decision-making awareness of hemodialysis nurses.

Pan-Indian Clinical Registry of Invasive Fungal Infections Among Patients in the Intensive Care Unit: Protocol for a Multicentric Prospective Study.

Fungal infections are now a great public health threat, especially in those with underlying risk factors such as neutropenia, diabetes, high-dose steroid treatment, cancer chemotherapy, prolonged intensive care unit stay, and so on, which can lead to mycoses with higher mortality rates. The rates of these infections have been steadily increasing over the past 2 decades due to the increasing population of patients who are immunocompromised. However, the data regarding the exact burden of such infection are still not available from India. Therefore, this registry was initiated to collate systematic data on invasive fungal infections (IFIs) across the country. The primary aim of this study is to create a multicenter digital clinical registry and monitor trends of IFIs and emerging fungal diseases, as well as early signals of any potential fungal outbreak in any region. The registry will also capture information on the antifungal resistance patterns and the contribution of fungal infections on overall morbidity and inpatient mortality across various conditions. This multicenter, prospective, noninterventional observational study will be conducted by the Indian Council of Medical Research through a web-based data collection method from 8 Advanced Mycology Diagnostic and Research Centers across the country. Data on age, gender, clinical signs and symptoms, date of admission, date of discharge or death, diagnostic tests performed, identified pathogen details, antifungal susceptibility testing, outcome, and so on will be obtained from hospital records. Descriptive and multivariate statistical methods will be applied to investigate clinical manifestations, risk variables, and treatment outcomes. These Advanced Mycology Diagnostic and Research Centers are expected to find the hidden cases of fungal infections in the intensive care unit setting. The study will facilitate the enhancement of the precision of fungal infection diagnosis and prompt treatment modalities in response to antifungal drug sensitivity tests. This registry will improve our understanding of IFIs, support evidence-based clinical decision-making ability, and encourage public health policies and actions. Fungal diseases are a neglected public health problem. Fewer diagnostic facilities, scanty published data, and increased vulnerable patient groups make the situation worse. This is the first systematic clinical registry of IFIs in India. Data generated from this registry will increase our understanding related to the diagnosis, treatment, and prevention of fungal diseases in India by addressing pertinent gaps in mycology. This initiative will ensure a visible impact on public health in the country. DERR1-10.2196/54672.

A predictive nomogram for surgical site infection in patients who received clean orthopedic surgery: a retrospective study

BackgroundSurgical site infection (SSI) is a common and serious complication of elective clean orthopedic surgery that can lead to severe adverse outcomes. However, the prognostic efficacy of the current staging systems remains uncertain for patients undergoing elective aseptic orthopedic procedures. This study aimed to identify high-risk factors independently associated with SSI and develop a nomogram prediction model to accurately predict the occurrence of SSI.MethodsA total of 20,960 patients underwent elective clean orthopedic surgery in our hospital between January 2020 and December 2021, of whom 39 developed SSI; we selected all 39 patients with a postoperative diagnosis of SSI and 305 patients who did not develop postoperative SSI for the final analysis. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were conducted in the training cohort to screen for independent risk factors of SSI, and a nomogram prediction model was developed. The predictive performance of the nomogram was compared with that of the National Nosocomial Infections Surveillance (NNIS) system. Decision curve analysis (DCA) was used to assess the clinical decision-making value of the nomogram.ResultsThe SSI incidence was 0.186%. Univariate and multivariate logistic regression analysis identified the American Society of Anesthesiology (ASA) class (odds ratio [OR] 1.564 [95% confidence interval (CI) 1.029–5.99, P = 0.046]), operative time (OR 1.003 [95% CI 1.006–1.019, P < 0.001]), and D-dimer level (OR 1.055 [95% CI 1.022–1.29, P = 0.046]) as risk factors for postoperative SSI. We constructed a nomogram prediction model based on these independent risk factors. In the training and validation cohorts, our predictive model had concordance indices (C-indices) of 0.777 (95% CI 0.672–0.882) and 0.732 (95% CI 0.603–0.861), respectively, both of which were superior to the C-indices of the NNIS system (0.668 and 0.543, respectively). Calibration curves and DCA confirmed that our nomogram model had good consistency and clinical predictive value, respectively.ConclusionsOperative time, ASA class, and D-dimer levels are important clinical predictive indicators of postoperative SSI in patients undergoing elective clean orthopedic surgery. The nomogram predictive model based on the three clinical features demonstrated strong predictive performance, calibration capabilities, and clinical decision-making abilities for SSI.

Classification and prognostic factors of patients with cervical spondylotic myelopathy after surgical treatment: a cluster analysis

Identifying potential prognostic factors of CSM patients could improve doctors’ clinical decision-making ability. The study retrospectively collected the baseline data of population characteristics, clinical symptoms, physical examination, neurological function and quality of life scores of patients with CSM based on the clinical big data research platform. The modified Japanese Orthopedic Association (mJOA) score and SF-36 score from the short-term follow-up data were entered into the cluster analysis to characterize postoperative residual symptoms and quality of life. Four clusters were yielded representing different patterns of residual symptoms and quality of patients’ life. Patients in cluster 2 (mJOA RR 55.8%) and cluster 4 (mJOA RR 55.8%) were substantially improved and had better quality of life. The influencing factors for the better prognosis of patients in cluster 2 were young age (50.1 ± 11.8), low incidence of disabling claudication (5.0%) and pathological signs (63.0%), and good preoperative SF36-physiological function score (73.1 ± 24.0) and mJOA socre (13.7 ± 2.8); and in cluster 4 the main influencing factor was low incidence of neck and shoulder pain (11.7%). We preliminarily verified the reliability of the clustering results with the long-term follow-up data and identified the preoperative features that were helpful to predict the prognosis of the patients. This study provided reference and research basis for further study with a larger sample data, extracting more patient features, selecting more follow-up nodes, and improving clustering algorithm.

Implementation of Competency-Based Education for Quality Midwifery Programs in Africa: Protocol for a Scoping Review.

Competency-based education (CBE) for midwifery programs is a system of academic instruction and evaluation that aims to achieve proficiency in midwifery student learning outcomes, which is based on students' ability to demonstrate the knowledge, attitudes, self-perceptions, and skills of a predetermined set of educational activities in theory and practice. CBE focuses on ensuring that midwifery students can develop critical thinking abilities, values, and the clinical decision-making abilities needed for the delivery of safe care in future practice. The objective of this scoping review is to map and synthesize existing literature on the implementation of CBE for midwifery programs and its sustainability in Africa. We will use the Arksey and O'Malley approach for scoping reviews for the research methodology. The 3-stage search process, proposed by the Joanna Briggs Institute, will be used to determine the eligibility of published and unpublished studies. PubMed, Science Direct, Web of Science, CINAHL, PsycINFO, and Scopus will be searched to screen published articles. ProQuest Dissertations and Theses and Google Scholar will be used to search for unpublished studies. Findings will only apply to studies conducted in Africa from 2010 to the present year in English. The 2 reviewers will work independently to carefully screen and compare the full text of the selected citations to the inclusion criteria. In the event of any disagreements between the 2 reviewers at any stage of the selection process regarding the inclusion of an article, this will be settled by discussion or consultation with a third reviewer. The extracted data will be presented using a PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review) flow diagram with an attached narrative summary. The review will summarize and disseminate findings on the implementation of the CBE for midwifery programs and its sustainability in Africa. It is intended that this scoping review will be completed within 6 months following the publication of this protocol. The conclusions from this scoping review will inform midwifery educators, institutions, policy makers, and other stakeholders on the strategies to implement and sustain CBE for midwifery programs in Africa. PRR1-10.2196/47603.

Development and Effects of a Virtual Reality Simulation Nursing Education Program Combined With Clinical Practice Based on an Information Processing Model.

The need to strengthen patient human rights and create a patient-centered healthcare environment is growing. Also as science and technology develop, new educational methods using virtual reality in nursing education are emerging. This study aimed to develop a virtual reality simulation nursing education program related to postoperative patient nursing based on an information processing model and to verify its effectiveness. Clinical practice-linked virtual reality simulation nursing education was conducted for a total of 4 weeks. Nursing students were divided into an experimental group (n = 22) experiencing virtual reality simulation combined with clinical practice and a control group (n = 22) having routine clinical practice. The analytical results of this study indicated that the information processing model-based virtual reality simulation nursing education program was effective in improving nursing students' performance confidence and clinical decision-making ability. Therefore, the virtual reality simulation program developed in this study can provide basic data for the development of a simulation curriculum in the future and can contribute to the development of clinical competency as a professional nurse by improving the performance confidence and clinical decision-making ability of nursing students.

How does ChatGPT-4 preform on non-English national medical licensing examination? An evaluation in Chinese language.

ChatGPT, an artificial intelligence (AI) system powered by large-scale language models, has garnered significant interest in healthcare. Its performance dependent on the quality and quantity of training data available for a specific language, with the majority of it being in English. Therefore, its effectiveness in processing the Chinese language, which has fewer data available, warrants further investigation. This study aims to assess the of ChatGPT's ability in medical education and clinical decision-making within the Chinese context. We utilized a dataset from the Chinese National Medical Licensing Examination (NMLE) to assess ChatGPT-4's proficiency in medical knowledge in Chinese. Performance indicators, including score, accuracy, and concordance (confirmation of answers through explanation), were employed to evaluate ChatGPT's effectiveness in both original and encoded medical questions. Additionally, we translated the original Chinese questions into English to explore potential avenues for improvement. ChatGPT scored 442/600 for original questions in Chinese, surpassing the passing threshold of 360/600. However, ChatGPT demonstrated reduced accuracy in addressing open-ended questions, with an overall accuracy rate of 47.7%. Despite this, ChatGPT displayed commendable consistency, achieving a 75% concordance rate across all case analysis questions. Moreover, translating Chinese case analysis questions into English yielded only marginal improvements in ChatGPT's performance (p = 0.728). ChatGPT exhibits remarkable precision and reliability when handling the NMLE in Chinese. Translation of NMLE questions from Chinese to English does not yield an improvement in ChatGPT's performance.

Evaluating the Clinical Decision-Making Ability of Large Language Models Using MKSAP-19 Cardiology Questions

Evaluating the Clinical Decision-Making Ability of Large Language Models Using MKSAP-19 Cardiology Questions

임상간호사의 공감능력과 간호조직문화인지가 임상의사결정능력에 미치는 영향

Objectives This study investigates the influence of clinical nurses' empathy ability and Cognition of nursing organizational culture on clinical decision-making ability.&#x0D; Methods Data collection was conducted on 198 clinical nurses at two general hospitals at P City from February10 to February 28th, 2023. The data responded to the survey were analyzed using descriptive analysis, t-test, One-way ANOVA, Pearson's correlation coefficient, and Hierarchical Multiple Regression Analysis.&#x0D; Results As results of this study, empathy ability scored 5.01 out of 7, Cognition of nursing organizational culture 3.47 out of 5, decision-making ability score was 3.56 out of 5. Empathy ability was significantly higher when the woker higher departmental experience (F=2.567, p&lt;.05), the higher position (F=3.662, p&lt;.05). Cognition of nursing organizational culture increased woker higher departmental experience (F=2.753, p&lt;.05) and higher the position (F=3.234, p&lt;.05). Clinical nurses' of clinical decision-making ability was correlated with empathy ability(r=.568, p&lt;.001) and there was a correlation with Cognition of nursing organizational culture (r=.467, p&lt;.001), there was a correlation with also correlated with empathy ability(r=.370, p&lt;.001). Empathy ability(β=.457, p&lt;.001) and Cognition of nursing organizational culture (β=.298, p&lt;.001) had an effect on clinical decision-making ability, explaining 39.9% of the variance. In other words, it was results show that higher levels of empathy ability and Cognition of nursing organizational culture were associated with enhanced clinical decision-making ability.&#x0D; Conclusions Based on the results of this study, there is a need of the development and application of programs to clinical decision-making ability improvement program to strengthen clinical competency of Clincal nurses.

Nursing students’ self-efficacy and clinical decision-making in the context of medication administration to children: A descriptive-correlational study

BackgroundIt is crucial for nursing students to possess self-efficacy in medication administration to children for correct drug implementation. It is expected that nursing students enhance their clinical application skills in the pediatric field before graduation, execute the clinical decision-making process, and acquire self-efficacy in medication administration. AimThis research was conducted to examine the self-efficacy and clinical decision-making levels of nursing students in the context of medication administration to children. MethodsThis descriptive-correlational study was conducted with 368 students. The data were collected using the “Participant Information Form”, the “Medication Administration Self-Efficacy Scale in Children for Nursing Students”, and the “Clinical Decision-Making in Nursing Scale”. Number, percentage, minimum and maximum values, mean and standard deviation, Independent Samples T-Test, ANOVA, and Pearson correlation test were used in the analysis of the data. In addition, Cronbach’s Alpha coefficient was calculated to evaluate the reliability of the scales. The research report of the study followed the STROBE guidelines. ResultsIn the study, the mean score of the students on the Clinical Decision-Making in Nursing Scale was slightly low; the mean score of the Medication Administration Self-Efficacy Scale in Children for Nursing Students was found to be moderate; and a significant and negative relationship was determined between the two scales. The clinical decision-making levels of the male students, the fourth-year nursing students, and those who did not experience anxiety during medication administration were found to be significantly higher. The self-efficacy of the students who did not experience anxiety during medication administration to children was found to be significantly higher than the others. ConclusionNursing students need to possess self-efficacy in administering medication to children and be able to make clinical decisions. Given the significant impact of the clinical decision-making process on patient outcomes and safety, it is necessary to develop educational programs to enhance the clinical decision-making abilities of nursing students. Different educational methods that will increase students’ self-efficacy in medication administration to children need to be implemented.

A machine learning model for distinguishing Kawasaki disease from sepsis

KD is an acute systemic vasculitis that most commonly affects children under 5 years old. Sepsis is a systemic inflammatory response syndrome caused by infection. The main clinical manifestations of both are fever, and laboratory tests include elevated WBC count, C-reactive protein, and procalcitonin. However, the two treatments are very different. Therefore, it is necessary to establish a dynamic nomogram based on clinical data to help clinicians make timely diagnoses and decision-making. In this study, we analyzed 299 KD patients and 309 sepsis patients. We collected patients' age, sex, height, weight, BMI, and 33 biological parameters of a routine blood test. After dividing the patients into a training set and validation set, the least absolute shrinkage and selection operator method, support vector machine and receiver operating characteristic curve were used to select significant factors and construct the nomogram. The performance of the nomogram was evaluated by discrimination and calibration. The decision curve analysis was used to assess the clinical usefulness of the nomogram. This nomogram shows that height, WBC, monocyte, eosinophil, lymphocyte to monocyte count ratio (LMR), PA, GGT and platelet are independent predictors of the KD diagnostic model. The c-index of the nomogram in the training set and validation is 0.926 and 0.878, which describes good discrimination. The nomogram is well calibrated. The decision curve analysis showed that the nomogram has better clinical application value and decision-making assistance ability. The nomogram has good performance of distinguishing KD from sepsis and is helpful for clinical pediatricians to make early clinical decisions.

The Effect of Expert Patient Simulation on Clinical Judgment: A Quasi-Experimental Study.

Worldwide, quality education is one of the important tools to improve healthcare quality. Healthcare practitioners must be competent in their clinical judgement to meet clients' need. However, poor clinical judgment skill accounts for almost one-third of all patient problems in health care. Expert patient simulation has been used as a training method for clinical judgement skill. However, according to empirical studies, using expert patients to develop clinical judgement skill is unclear. The method is effective in one situation but not in another. To examine the effect of expert patient simulation on the clinical judgment skill of health science students of Mizan-Tepi University. A pre-test/post-test quasi-experimental design was used on 92 randomly selected samples from the graduating cohort of midwifery students. The research subjects who took part in the experiment were picked at random. Tools included the Creighton Competency Evaluation Instrument (C-CEI®), the Learning Satisfaction and Self-Confidence Questionnaire, and the Kolb Learning Style Inventory (LSI). The Wilcoxon-signed rank test was utilized to compare the self-confidence scores among intervention and control group of students, and the paired sample test was used to compare clinical judgment scores. Cohen's d was used to assess the effect size, and Spearman correlation was used to explore the association. Clinical decision-making ability and self-confidence measures revealed statistical and practical differences between before and after simulation. There was a mean difference of 2.28 (95% CI, 1.78, 2.79), t (45)=9.13, p 0.001, and an effect size of 1.3, p 0.001. A pre-and post-simulation self-confidence measure showed statistically significant improvement after simulation (W = 1, Z = -3.57, P 0.001). A moderately significant positive connection (r = 0.419, p 0.004) was also discovered. The study found that human expert patient simulation is a tremendous clinical training technique for improving students' clinical decision-making skill competency and self-confidence.

18F‑FDG PET/CT based radiomics features improve prediction of prognosis: multiple machine learning algorithms and multimodality applications for multiple myeloma

PurposeMultiple myeloma (MM), the second most hematological malignancy, have been studied extensively in the prognosis of the clinical parameters, however there are only a few studies have discussed the role of dual modalities and multiple algorithms of 18F-FDG (18F-fluorodeoxyglucose) PET/CT based radiomics signatures for prognosis in MM patients. We hope to deeply mine the utility of raiomics data in the prognosis of MM.MethodsWe extensively explored the predictive ability and clinical decision-making ability of different combination image data of PET, CT, clinical parameters and six machine learning algorithms, Cox proportional hazards model (Cox), linear gradient boosting models based on Cox’s partial likelihood (GB-Cox), Cox model by likelihood based boosting (CoxBoost), generalized boosted regression modelling (GBM), random forests for survival model (RFS) and support vector regression for censored data model (SVCR). And the model evaluation methods include Harrell concordance index, time dependent receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsWe finally confirmed 5 PET based features, and 4 CT based features, as well as 6 clinical derived features significantly related to progression free survival (PFS) and we included them in the model construction. In various modalities combinations, RSF and GBM algorithms significantly improved the accuracy and clinical net benefit of predicting prognosis compared with other algorithms. For all combinations of various modalities based models, single-modality PET based prognostic models’ performance was outperformed baseline clinical parameters based models, while the performance of models of PET and CT combined with clinical parameters was significantly improved in various algorithms.Conclusion18F‑FDG PET/CT based radiomics models implemented with machine learning algorithms can significantly improve the clinical prediction of progress and increased clinical benefits providing prospects for clinical prognostic stratification for precision treatment as well as new research areas.

Nursing Students' Fear of Negative Evaluation and Perceptions of Clinical Decision-Making.

Nursing is a profession based on theoretical knowledge and practice, and the clinical decision-making process is important. Many factors affect the fear of negative evaluation, and fear of a negative evaluation is a potential variable that can affect clinical decision-making. This descriptive cross-sectional study included undergraduate nursing students (n = 283). Nursing students' fear of a negative evaluation and clinical decision-making scale scores were 31.92 ± 08.51 and 149.18 ± 13.67, respectively. No significant relationship was identified between the scores (p > .05). The fear of a negative evaluation was not associated with nursing students' perceptions of clinical decision-making. To reduce nursing students' fear of a negative evaluation and improve their clinical decision-making ability, appropriate training programs should be developed and implemented by nursing educators and administrators. [J Nurs Educ. 2023;62(6):325-331.].

Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study.

Metastasis in the lungs is common in patients with rectal cancer, and it can have severe consequences on their survival and quality of life. Therefore, it is essential to identify patients who may be at risk of developing lung metastasis from rectal cancer. In this study, we utilized eight machine-learning methods to create a model for predicting the risk of lung metastasis in patients with rectal cancer. Our cohort consisted of 27,180 rectal cancer patients selected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017 for model development. Additionally, we validated our models using 1118 rectal cancer patients from a Chinese hospital to evaluate model performance and generalizability. We assessed our models' performance using various metrics, including the area under the curve (AUC), the area under the precision-recall curve (AUPR), the Matthews Correlation Coefficient (MCC), decision curve analysis (DCA), and calibration curves. Finally, we applied the best model to develop a web-based calculator for predicting the risk of lung metastasis in patients with rectal cancer. Our study employed tenfold cross-validation to assess the performance of eight machine-learning models for predicting the risk of lung metastasis in patients with rectal cancer. The AUC values ranged from 0.73 to 0.96 in the training set, with the extreme gradient boosting (XGB) model achieving the highest AUC value of 0.96. Moreover, the XGB model obtained the best AUPR and MCC in the training set, reaching 0.98 and 0.88, respectively. We found that the XGB model demonstrated the best predictive power, achieving an AUC of 0.87, an AUPR of 0.60, an accuracy of 0.92, and a sensitivity of 0.93 in the internal test set. Furthermore, the XGB model was evaluated in the external test set and achieved an AUC of 0.91, an AUPR of 0.63, an accuracy of 0.93, a sensitivity of 0.92, and a specificity of 0.93. The XGB model obtained the highest MCC in the internal test set and external validation set, with 0.61 and 0.68, respectively. Based on the DCA and calibration curve analysis, the XGB model had better clinical decision-making ability and predictive power than the other seven models. Lastly, we developed an online web calculator using the XGB model to assist doctors in making informed decisions and to facilitate the model's wider adoption (https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py). In this study, we developed an XGB model based on clinicopathological information to predict the risk of lung metastasis in patients with rectal cancer, which may help physicians make clinical decisions.

People with dry age‐related macular degeneration; views on certification of vision impairment in a primary care setting: An explorative qualitative study

Evidence supports the clinical decision-making ability of primary care optometrists with additional qualifications in the identification of eligibility criteria for the certification of vision impairment (CVI). Welsh Government policy is driving pathway change to enable these optometrists to perform CVI. This qualitative study explores the views of people with VI due to dry age-related macular degeneration (AMD) on this pathway change. Nine people with VI due to dry AMD, attending Macular Society support groups, participated. Individual semi-structured interviews were conducted and analysed concurrently using thematic analysis. Five major themes were identified: (1) living with dry AMD, (2) experience of eye care provision, (3) knowledge of CVI, (4) provision of information and (5) CVI in primary care. Participants consistently highlighted the need for the provision of accessible information regarding the certification pathway, dry AMD and the optometrist's role in the provision of eye health care. Information needs to be available prior to the diagnosis of an eye disease, rather than only from the point of diagnosis or where the vision reaches the level required for certification. The findings support the provision of CVI within primary eye care while highlighting areas of importance in pathway development. These include the provision of accessible information prior to, at the point of and following the diagnosis of an eye condition. Information provided needs to extend to the awareness of the role of the optometrist in the provision of eye care, and public health awareness of modifiable risk factors, which will influence the possibility of disease development in later life. The findings provide information that will be useful to those responsible for the provision of CVI within primary care.

Effectiveness of the Patient's Severity Classification Competency Promotion Virtual Reality Program of Nursing Students during the COVID-19 Pandemic Period.

The purpose of this study was to develop a virtual reality-based nursing education program aimed at improving nursing students' severity classification competency. Severity classification in the emergency room is key to improving the efficiency of emergency room services worldwide. Prioritizing treatment based on correctly identifying the severity of a disease or an injury also ensures patients' safety. The five actual clinical scenarios in the program helped to promptly classify patients into five clinical situations based on the 2021 Korean Emergency Patient Classification Tool. Seventeen nursing students were in an experimental group that had access to a virtual reality-based simulation combined with clinical practice. Seventeen nursing students were in a control group that only participated in routine clinical practice. The virtual reality-based nursing education program effectively improved students' severity classification competency, performance confidence, and clinical decision-making ability. Although the pandemic continues, the virtual reality-based nursing education program provides realistic indirect experiences to nursing students in situations where clinical nursing practice is not possible. In particular, it will serve as basic data for the expansion and utilization strategy of virtual reality-based nursing education programs to improve nursing capabilities.

Clinical-radiomic analysis for non-invasive prediction of liver steatosis on non-contrast CT: A pilot study.

Purpose: Our aim is to build and validate a clinical-radiomic model for non-invasive liver steatosis prediction based on non-contrast computed tomography (CT). Methods: We retrospectively reviewed 342 patients with suspected NAFLD diagnoses between January 2019 and July 2020 who underwent non-contrast CT and liver biopsy. Radiomics features from hepatic and splenic regions-of-interests (ROIs) were extracted based on abdominal non-contrast CT imaging. The radiomics signature was constructed based on reproducible features by adopting the least absolute shrinkage and selection operator (LASSO) regression. Then, multivariate logistic regression analysis was applied to develop a combined clinical-radiomic nomogram integrating radiomics signature with several independent clinical predictors in a training cohort of 124 patients between January 2019 and December 2019. The performance of models was determined by the area under the receiver operating characteristic curves and calibration curves. We conducted an internal validation during 103 consecutive patients between January 2020 and July 2020. Results: The radiomics signature was composed of four steatosis-related features and positively correlated with pathologic liver steatosis grade (p < 0.01). In both subgroups (Group One, none vs. steatosis; Group Two, none/mild vs. moderate/severe steatosis), the clinical-radiomic model performed best within the validation cohort with an AUC of 0.734 and 0.930, respectively. The calibration curve confirmed the concordance of excellent models. Conclusion: We developed a robust clinical-radiomic model for accurate liver steatosis stage prediction in a non-invasive way, which may improve the clinical decision-making ability.