Decision-making Outcomes Research Articles

Disparities in clinical trial enrollment among LGBTQ+ individuals and its impact on cancer treatment and management: A systematic review.

805 Background: There is a significant gap in oncologic research, which arises from the exclusion of LGBTQ+ individuals from cancer clinical trials, compromising the development of personalized cancer therapies and limiting the generalizability of findings. Despite known health disparities in this population, including higher cancer risk factors and unique psychosocial stressors, data on clinical trial participation remains sparse. This study aims to elucidate the extent of underrepresentation of LGBTQ+ individuals in cancer clinical trials and examine its impact on treatment efficacy, safety, and overall management. Methods: We conducted a systematic review of literature and clinical trial databases, including PubMed, ClinicalTrials.gov, and other registries, focusing on cancer trials from 2010 to 2024. We extracted data on LGBTQ+ enrollment, analyzed demographic inclusivity criteria, and identified barriers to participation. Subgroup analyses evaluated the correlation between LGBTQ+ representation and treatment outcomes, considering variables such as trial design, recruitment strategies, and data collection on sexual orientation and gender identity. The review also examined policy changes and inclusivity initiatives aimed at improving trial access for this community. Results: The review identified a significant underrepresentation of LGBTQ+ individuals in cancer trials, with sexual orientation and gender identity data reported in only 6.2% of trials. Structural and sociocultural barriers, including heteronormative trial designs, lack of inclusive recruitment strategies, and pervasive discrimination, were frequently cited as impediments. Trials that included LGBTQ+ participants rarely conducted stratified analyses to assess differential treatment responses, leading to a paucity of data on drug safety, efficacy, and toxicity profiles specific to this population. The absence of these data may contribute to suboptimal therapeutic decision-making and potential adverse outcomes in clinical practice. Conclusions: The substantial underrepresentation of LGBTQ+ individuals in cancer clinical trials impairs the ability to develop inclusive, evidence-based oncology care. Addressing this gap requires systematic changes, including the integration of sexual and gender minority data collection, implementation of LGBTQ+-specific recruitment frameworks, and stratified analyses of treatment outcomes. Enhancing trial inclusivity will not only improve the external validity of oncologic research but also support the development of tailored therapeutic approaches that address the unique needs of LGBTQ+ cancer patients, ultimately advancing health equity in cancer care.

Systematic, Pretransplant Screening by Aortoiliac CT Angiography: Impact on Surgical Decision-making and Clinical Outcomes.

Aortoiliac screening before kidney transplantation is suggested by some guidelines to select patients for transplantation and to assist surgical planning. We investigated the clinical outcomes of systematic screening for aortoiliac disease in potential kidney transplant candidates. In this observational study, 470 potential kidney transplant candidates underwent aortoiliac computed tomography angiography. Patients were characterized by the presence of peripheral artery disease and calcification of iliac arteries and aortoiliac arteries. The risk of graft loss and graft function at 1 y posttransplant were examined and clinical decisions based on the vascular findings were assessed. Clinically diagnosed peripheral artery disease was present in 66 patients (14%), circular calcifications in 101 patients (21%), and aortoiliac stenosis in 77 patients (16%). In 326 patients undergoing kidney transplantation, circular calcification or aortoiliac stenosis was not associated with an increased risk of graft loss (P = 0.45 and P = 0.28) or estimated glomerular filtration rate (P = 0.23 and P = 0.76) at 1 y posttransplant. When evaluated for transplantability, clinical decision-making based on vascular findings was recorded in 67 of 429 patients (16%), including rejection for transplantation in 7 patients (2%) and laterality for surgical implantation in 52 patients (12%). Systematic screening by aortoiliac computed tomography angiography may assist in surgical planning but seems of limited clinical value in assessing the risk of future graft loss and graft function in patients undergoing kidney transplantation.

EXPLORING THE IMPACT OF EDUCATIONAL GAMES ON NUMERACY SKILL DEVELOPMENT AMONG THE ELEMENTARY LEARNERS: A SYSTEMATIC REVIEW

Numeracy abilities are crucial because they help people make educated decisions in their daily lives. These skills are especially important in fields such as health and finance, where proper risk interpretation is required. Individuals who lack numeracy abilities may struggle to grasp and analyze information, which can have a detrimental impact on their decision-making and life outcomes. Numeracy abilities are especially vital for university preparation because many courses need some level of mathematical literacy. Employers also value numeracy skills in graduate recruitment, as numeracy tests are frequently employed in the hiring process. Furthermore, numeracy abilities are strongly linked to their practical application, and they play an important role in many aspects of individual well-being. Overall, numeracy skills are important for everyday life, academic success, and career opportunities.

Multidimensional Analysis of Preoperative Patient-Reported Outcomes Identifies Distinct Phenotypes in Total Knee Arthroplasty: Secondary Analysis of the SHARKS Registry in a Public Hospital Department.

Traditional research on total knee arthroplasty (TKA) relies on preoperative patient-reported outcome measures (PROMs) to predict postoperative satisfaction. We aim to identify distinct patient phenotypes among TKA candidates, and investigate their correlations with patient characteristics. Between 2017 and 2021, patients with primary knee cases at a metropolitan public hospital were enrolled in a clinical quality registry. Demographics, clinical data, and the Veterans Rand 12 and Oxford Knee Score were collected. Imputed data were utilized for the primary analysis, employing k-means clustering to identify four phenotypes. Analysis of variance assessed differences in scores between clusters, and nominal logistic regression determined relationships between phenotypes and patient age, sex, body mass index (BMI), and laterality. The sample comprised 389 patients with 450 primary knees. Phenotype 4 (mild symptoms with good mental health) exhibited superior physical function and overall health. In contrast, patients in phenotype 2 (severe symptoms with poor mental health) experienced the most knee pain and health issues. Phenotype 1 (moderate symptoms with good mental health) reported high mental health scores despite knee pain and physical impairment. Patient characteristics significantly correlated with phenotypes; those in the severe symptoms with poor mental health phenotype were more likely to be younger, female, have a higher BMI, and bilateral osteoarthritis (p < 0.05). This multidimensional analysis identified TKA patient phenotypes based on common PROMs, revealing associations with patient demographics. This approach has the potential to inform prognostic models, enhancing clinical decision-making and patient outcomes in joint replacement.

Upright versus recumbent lumbar spine MRI: Do findings differ systematically, and which correlates better with pain? A systematic review.

Recumbent MRI is the most widely used image modality in people with low back pain (LBP), however, it has been proposed that upright (standing) MRI has advantages over recumbent MRI because of its ability to assess the effects of being weight-bearing. It has been suggested that this produces systematic differences in MRI parameters and differences in the correlation between MRI parameters and pain or disability in patients thus, potentially adding clinically helpful information. This paper aims to review and summarize the available empirical evidence for or against these two hypotheses. Systematic review of the literature (PROSPERO ID: CRD42017048318). Studies should be based on paired observations of MRI findings in the upright and recumbent positions. Studies needed a minimum of 15 participants. People aged 18 or older with or without low back pain ± radiculopathy OUTCOME MEASURES: All continuous, ordinal, and dichotomous parameters based on MRI images. All measures of pain or disability. Studies assessing MRI parameters both in upright and recumbent positions on the same individuals measured on continuous, ordinal, or dichotomous scales were included. For each parameter, the expected direction of the difference between recumbent and upright position was specified as an increase, no change, or decrease. Information on the observed distribution of individual differences was extracted from included studies and subjected to meta-analyses if sufficient data was available. Observed differences were then compared with the prespecified expectations. Studies were also screened for information on correlations between patients' pain and/or disability and MRI parameters or differences between patient subgroups defined by patients' pain and/or disability. 19 studies were identified, including 5.082 participants with LBP (16 studies) and 166 participants without low back pain (5 studies). Twenty-five MRI parameters were measured on a continuous scale, ten parameters were assessed on an ordinal scale, and 15 parameters were reported as dichotomous data. The observed differences between recumbent and upright MRI were mostly consistent with the prespecified expectations. Correlations between patients' pain or disability level and MRI parameters were reported in only one study, and three studies reported comparisons of MRI parameters across subgroups of patients defined by pain or disability characteristics. Higher correlations or larger effect sizes when using the upright position were observed in most results reported. For most MRI parameters, the direction of the observed difference between assessment in recumbent and upright positions aligned with the pre-specified expectation implied by the weight-bearing position. This confirms the existence of a systematic difference between the two positions. Performing an MRI upright instead of recumbent position may increase the correlation with pain, but final evidence for this property is still missing. The clinical significance of upright MRI is still unclear, and there is a need to directly investigate the impact of MRI findings on clinical decision-making and patient outcomes.

Evaluating the Adoption of Evidence-Based Management Practices in Eye Hospitals

Background: Delivering sustainable, high-quality eye care requires a comprehensive understanding of patient conditions, clinical evidence, patients’ preferences, demand patterns, quality supplies, outcomes, financial sustainability, and satisfaction metrics. Evidence-based management (EBM) offers a structured approach to align actions with evidence, enabling effective decision-making and better organizational outcomes. Evaluating current practices against EBM principles fosters awareness and promotes an EBM culture in eye hospitals, supporting improved and sustainable service delivery. Methods: A descriptive cross-sectional survey was conducted in 2023–2024 among 94 eye hospitals worldwide, selected from two networks, using the Centre for Evidence-Based Management (CEBM) assessment questionnaire. Follow-up reminders resulted in 43 responses. Data analysis utilized frequency distributions and Pearson’s correlation to explore relationships between variables. Results: A strong positive correlation was observed between data accessibility and the capacity to interpret data in fostering evidence-based decision-making (r = 0.69, p &lt; 0.01). Additionally, leveraging internal expertise and engaging stakeholders in assessing and utilizing data showed a moderate association with EBM practices (r = 0.48, p &lt; 0.01). Conclusions: Eye hospitals demonstrate alignment with EBM principles, though regional variations exist. Organizations with robust data utilization systems, analytical expertise, and a commitment to continuous improvement are more effective in practicing EBM. Educational and peer-learning initiatives can further support hospitals in adopting EBM principles, strengthening their capacity for evidence-based decision-making, and enhancing eye care services.

P1055 Refusing Stoma-Forming Surgery: Preliminary Findings from an Interpretative Phenomenological Analysis of Patients with Inflammatory Bowel Disease

Abstract Background Stoma-forming surgery (SFS) is often viewed as a last resort for managing severe Inflammatory Bowel Disease (IBD), encompassing Crohn’s Disease (CD) and Ulcerative Colitis (UC) (1). Despite its potential life-saving benefits, many patients refuse this intervention due to psychological, social, and cultural factors. While substantial research has explored the experiences of individuals living with a stoma, less is known about the perspectives of those who decline this procedure (2,3). This study investigates the decision-making process and lived experiences of IBD patients who refuse SFS. To explore the psychological, social, and contextual factors influencing IBD patients’ refusal of SFS and to understand how they navigate their disease while rejecting this treatment option. Methods Using Interpretative Phenomenological Analysis (IPA), semi-structured interviews were conducted with seven adult IBD patients (3 men, and 4 women) who declined SFS. Participants were recruited from a gastroenterology department and online forums. Interviews focused on their disease experience, decision-making processes, and perceptions of life with and without a stoma. Verbatim transcripts were analyzed to identify emergent themes. Results Preliminary analysis revealed four main themes: 1. Personal Perception: Fear of lifestyle changes, social stigma, and surgery; mixed feelings of acceptance and coping. 2. External Factors: Influences from healthcare professionals, diagnosis, and treatment processes. 3. Educational Gaps: Inadequate or improper information provided about SFS. 4. Hope: Optimism for disease remission and new treatment options. A recurring observation was a tendency towards “magical thinking,” where patients avoided discussing SFS to deny its existence. Despite rejecting surgery, participants expressed ambivalence and ongoing struggles with their decision. Conclusion Patients’ refusal of SFS is multifaceted, influenced by personal biases, external circumstances, and a lack of tailored education. Addressing these concerns through comprehensive psychological support and patient-centered communication may improve decision-making and treatment outcomes for this vulnerable population.

Predicting postoperative adhesive small bowel obstruction in infants under 3 months with intestinal malrotation: a random forest approach.

This study aimed to develop a predictive model using a random forest algorithm to determine the likelihood of postoperative adhesive small bowel obstruction (ASBO) in infants under 3 months with intestinal malrotation. A machine learning model was used to predict postoperative adhesive small bowel obstruction using comprehensive clinical data extracted from 107 patients with a follow-up of at least 24 months. The Boruta algorithm was used for selecting clinical features, and nested cross-validation tuned and selected hyper-parameters for the random forest model. The model's performance was validated with 1000 bootstrap samples and assessed using receiver operating characteristic (ROC) analysis, the area under the ROC curve (AUC), sensitivity, specificity, precision, and F1 score. The random forest model demonstrated high diagnostic accuracy with an AUC of 0.960. Significant predictors of ASBO included pre-operative white blood cell count (pre-WBC), mechanical ventilation (MV) duration, surgery duration, and post-operative albumin levels (post-ALB). Partial dependence plots showed non-linear relationships and threshold effects for these variables. The model achieved high sensitivity (0.805) and specificity (0.952), along with excellent precision (0.809) and a robust F1 score (0.799), indicating balanced recall and precision performance. This study presents a machine learning model to accurately predict postoperative ASBO in infants with intestinal malrotation. Demonstrating high accuracy and robustness, this model shows great promise for enhancing clinical decision-making and patient outcomes in pediatric surgery.

Predicting Postoperative Mortality in Head Injury Patients: Evaluating the Accuracy of Rotterdam Score

Introduction: Head injury remains a leading cause of mortality and morbidity globally, necessitating accurate prognostic tools to guide clinical decision-making and inform patient outcomes. Rotterdam Score, a computed tomography (CT)-based scoring system, has shown promise in predicting mortality in head injury patients. This study aimed to evaluate the accuracy of the Rotterdam Score in predicting postoperative mortality in head injury patients undergoing surgery. Methods: A retrospective analysis was conducted on 56 head injury patients who underwent surgery at Dr. Mohammad Hoesin General Hospital, Palembang, between December 2023 and November 2024. Patient demographics, clinical characteristics, and CT scan findings were collected. Rotterdam Score was calculated for each patient, and its accuracy in predicting postoperative mortality was assessed using receiver operating characteristic (ROC) curve analysis. Results: The study cohort comprised 37 (66.1%) males and 19 (33.9%) females, with a mean age of 31.8 ± 21.6 years. Mild head injury was the most common Glasgow Coma Scale (GCS) classification (42.9%). The overall mortality rate was 17.8%. ROC curve analysis revealed an area under the curve (AUC) of 0.953 for the Rotterdam Score, with an optimal cut-off value of 4.5. Rotterdam Score demonstrated a sensitivity of 80%, specificity of 97.8%, positive predictive value (PPV) of 88.8%, and negative predictive value (NPV) of 95.7% in predicting postoperative mortality. Conclusion: The Rotterdam Score is a highly accurate predictor of postoperative mortality in head injury patients undergoing surgery. Its CT-based assessment allows for rapid and objective prognostication, aiding clinicians in risk stratification and treatment planning. Further research with larger and more diverse populations is warranted to validate these findings and establish the generalizability of Rotterdam Score across different healthcare settings.

Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques—GMDH-type neural network, gene expression programming, and combinatorial algorithm—are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964–0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor.

High Sensitivity of Fine-Needle Aspiration Calcitonin in Detecting Medullary Thyroid Carcinoma Is Independent of Predefined Decisional Thresholds.

The measurement of Calcitonin (Ctn) in fine-needle aspiration (FNA) washout fluids (FNA-Ctn) has demonstrated excellent sensitivity, significantly higher than FNA cytology, in detecting medullary thyroid carcinoma (MTC). However, the absence of a fixed cutoff value for FNA-Ctn poses a limitation. This study aimed to investigate whether the sensitivity of FNA-Ctn in detecting MTC varies with different cutoffs reported in the literature. A single-centre series of MTCs was retrospectively reviewed. The preoperative FNA-Ctn levels were re-evaluated using various thresholds previously reported in the literature, and the corresponding FNA-Ctn sensitivities were compared. Twenty-one MTCs were included (69% women; median age 59 years; median serum Ctn value 86 pg/mL; median MTC major diameter 10 mm). The median FNA-Ctn value was 2000 pg/mL (interquartile range 49-250). MTCs nodules were assessed at high risk (ACR TI-RADS 5) in 50% of cases, while 47.6% were cytologically malignant. Additionally, 42.9% of cases were assessed as stage III according to Union for International Cancer Control staging system (UICC). Serum Ctn was significantly lower in stage I (p = 0.04). FNA-Ctn was positively correlated with serum Ctn (Rho = 0.45; p = 0.04), while ACR TI-RADS assessment with MTC stage (Rho = 0.69; p = 0.003). FNA-Ctn sensitivity ranged from 95% to 100% based on the previously proposed FNA-Ctn cutoffs. The high sensitivity of FNA-Ctn in detecting MTC did not significantly differ when applying the previously proposed cutoffs. Given the absence of a universally applicable FNA-Ctn decisional threshold, institutions should establish their own diagnostic cutoffs. Future guidelines should incorporate these concepts to enhance clinical decision-making and patient outcomes.

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.

Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjögren's syndrome. Traditional evaluations often underestimate the associated cardiovascular disease (CVD) and stroke risk in women having AD. Vitamin D deficiency increases susceptibility to these conditions. CVD risk prediction in AD can benefit from surrogate biomarker for coronary artery disease (CAD), such as carotid ultrasound. Due to non-linearity in the CVD risk stratification, we use artificial intelligence-based system using AD biomarkers and carotid ultrasound. Investigate the relationship between AD and CVD/stroke markers including autoantibody-influenced plaque load. Second, to study the surrogate biomarkers for the CAD and gather radiomics-based features such as carotid intima-media thickness (cIMT), and plaque area (PA). Third and final, explore the automated CVD/stroke risk identification using advanced machine learning (ML) and deep learning (DL) paradigms. Analysed biomarker data from women with AD, including carotid ultrasonography imaging, clinical parameters, autoantibody profiles, and vitamin D levels. Proposed artificial intelligence (AI) models to predict CVD/stroke risk accurately in AD for women. There is a strong association between AD duration and elevated cIMT/PA, with increased CVD risk linked to higher rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPAs) levels. AI models outperformed conventional methods by integrating imaging data and disorder-specific factors. Interdisciplinary collaboration is crucial for managing CVD/stroke in women with chronic autoimmune diseases. AI-based assisted risk stratification methods may improve treatment decision-making and cardiovascular outcomes.

Open up the poverty loop: The influence of social class on intertemporal choice

Research has indicated that poverty and inequality among social classes can be a processive and intergenerational problem. Drawing from class-based social cognitive theory and cognitive-experiential self-theory, we propose that socioeconomic status (both subjective and objective) is linked to individual intertemporal decision-making through financial literacy and rational thinking. We tested our proposed theoretical relationships through two studies: an analysis of a large archival dataset, as well as a field survey. First, using CART analysis, we find that financial literacy and objective socioeconomic status possess predictive power over intertemporal choice outcomes: as an individual's financial literacy and objective socioeconomic hierarchy increase, so does their likelihood of saving money in the present rather than spending it. Furthermore, our findings indicate that financial literacy and rational thinking serve as mediators between social class and intertemporal choice. The results provide a more sophisticated view of how social class shapes intertemporal preference and decision-making outcomes.

Multinodular and Vacuolating Neuronal Tumor – Challenges and Perspectives

A multinodular and vacuolating neuronal tumor (MVNT) is a rare non-malign central nervous system tumor. The cerebral cortex of young to middle-aged adults is affected and seizures and focal neurological deficits could reveal it. Despite being considered a low-grade tumor, MVNT can be challenging to diagnose and manage due to its histopathological similarities to other neuronal tumors. The limited number of reported cases makes it difficult to establish definitive guidelines for treatment and follow-up. Given the relatively recent discovery of MVNT, there is a need for further research to better understand its pathogenesis, optimal treatment and long-term strategies. Furthermore, studying MVNT can contribute to the broader understanding of brain tumor biology and the intricacies of tumor heterogeneity. This paper explores the challenges and perspectives surrounding MVNT to improve clinical decision-making and patient outcomes. Based on the present narrative review, advancements in diagnostic technologies have shown promise in addressing the challenges associated with diagnosing MVNTs. Liquid biopsy is an innovative and non-invasive diagnostic tool that analyzes biomarkers to provide information about tumors, and radiomics is a method that extracts quantitative data from medical images to provide detailed insights into tumor characteristics. Such innovative diagnostic technologies are important because they have the potential to significantly improve the current diagnostic landscape for MVNTs, allowing for earlier detection, accurate classification, and personalized treatment strategies.

Automated Quantification of Vesicoureteral Reflux using Machine Learning with Advancing Diagnostic Precision

This article uses machine learning to quantify vesicoureteral reflux (VUR). VCUGs in pediatric urology are used to diagnose VUR. The goal is to increase diagnostic precision. Various machine learning models categorize VUR grades (Grade 1 to Grade 5) and are evaluated using performance metrics and confusion matrices. Study datasets come from internet repositories with repository names and accession numbers. Machine learning models performed well across several measures. KNN, Random Forest, AdaBoost, and CN2 Rule Induction consistently scored 100% in AUC, CA, F1-score, precision, recall, MCC, and specificity. These models classified grades well individually and collectively. In contrast, the Constant model performed poorly across all criteria, suggesting its inability to categorize VUR grades reliably. With the most excellent average performance ratings, the CN2 Rule Induction model excelled at grade categorization. Confusion matrices demonstrate that machine learning models predict VUR grades. The large diagonal numbers of the matrices show that the models are regularly predicted effectively. However, the Constant model's constant Grade 5 forecast reduced its differentiation. This study shows that most machine learning methods automate VUR measurement. The findings aid objective pediatric urology grading and radiographic evaluation. The CN2 Rule Induction model accurately classifies VUR grades. Machine learning-based diagnostic techniques may increase diagnostic precision, clinical decision-making, and patient outcomes.

The influence of body posture on cervical alignment measured in the sagittal plane on conventional radiographs: A systematic review.

Cervical sagittal balance parameters are an important aid in surgical decision-making and influence outcome in cervical spine surgery. In current literature, the normative values of these parameters vary highly within and between patients. This variability might be attributed to body posture. The primary aim is to review the literature on the influence of body posture, including studies that compare sitting or standing positions possibly varying in arm positions, on cervical alignment measured in the sagittal plane on X-rays. A systematic review was performed. PubMed, Embase, Cochrane Library, Web of Science, and CINAHL were systematically searched for articles published up to and including May 2024. The primary outcome was variability in cervical alignment in the sagittal plane as measured in altering body postures on X-rays. Quality of the included articles was assessed with the QUADAS-2 tool. Out of 17,628 screened articles, eight were eligible for inclusion. Articles were excluded based on irrelevant outcomes (n= 10,372), duplicates (n= 4,315), wrong study design (n= 1,462), or the influence of body posture was not described (n= 612). Overall cervical alignment ranged from 33.3 degrees lordosis to 3.5 degrees kyphosis. In all studies, statistically significant differences in T1 Slope (T1S), C2C7 Sagittal Vertical Axis (SVA), McGregor Slope (McGS), or C7 SVA were observed with respect to body posture. The observed differences between postures for cervical alignment ranged from 1 to 16.6 degrees. Body posture significantly influences cervical alignment values measured in the sagittal plane on X-rays. Studies investigating this influence are scarce and vary highly in investigated body postures. Standardization of body posture is imperative to enable optimal comparison of cervical sagittal balance parameters within and between patients, but also between studies. Moreover, the high variability observed raises questions about the comparability of measured values in previously published studies.

Evaluating the Corrected Femoroepiphyseal Acetabular Roof Index in Patients With Borderline Dysplasia Undergoing Periacetabular Osteotomy or Hip Arthroscopy.

Identifying hip instability in symptomatic patients with borderline dysplasia of the hip (BDH) is of paramount importance, as it can influence both surgical decision-making and surgical outcomes. The femoroepiphyseal acetabular roof (FEAR) index is strongly affected by the hip adduction/abduction angle during the pelvic radiograph, which has not yet been considered in the recommended threshold values. To compare the corrected FEAR index in symptomatic patients with BDH treated with pelvic periacetabular osteotomy (PAO) or hip arthroscopy. Cohort study; Level of evidence, 3. Patients with symptomatic hips and radiographical BDH were categorized into 2 cohorts. The first cohort included patients treated with PAO (n = 42) and the second cohort consisted of patients treated with hip arthroscopy due to symptomatic femoroacetabular impingement (n = 50). All patients presented with good patient-reported outcomes at the final follow-up. The FEAR index was measured on the pelvic radiograph at the initial hip adduction/abduction angle (uncorrected FEAR index) and after correcting the hip abduction angle to 0° (corrected FEAR index). Negative values of the FEAR index represent a lateral closing angle, whereas positive values represent a lateral opening angle. As for the hip adduction/abduction angle, negative values represent adduction, whereas positive values represent abduction. The corrected FEAR index varied significantly from the uncorrected FEAR index in both groups with a mean difference of 6°± 4° in patients treated with PAO and 5°± 5° in patients treated with hip arthroscopy. The corrected FEAR index in patients with BDH treated with hip arthroscopy (-11°± 8°) was significantly lower (P < .001) compared with the patients with BDH treated with PAO (-7°± 7°) (Table 1). The optimal threshold for the corrected FEAR index was -13° (odds ratio, 7.8 [95% CI, 2.6-23.1]; P < .001), which yielded a sensitivity of 85% and a specificity of 52%, distinguishing the 2 surgical cohorts. The corrected FEAR index might vary significantly from the uncorrected FEAR index, which is highly dependent on the hip adduction/abduction angle during the pelvic radiograph. Symptomatic patients with BDH treated with PAO exhibit a corrected FEAR index of ≥-13° compared with those with BDH treated with hip arthroscopy for impingement symptomatology.

Game-Based Adaptive Learning in Probability Education

Traditional approaches often fall short in effectively teaching complex subjects like probability and dynamic programming, especially in contexts requiring high engagement and individualized learning paths. This paper presents a simulation-based experimental study exploring the potential of an Artificial Intelligence (AI) adaptive learning system through the development of a game-based learning tool. The system utilizes dynamic programming principles and decision tree regressors to adjust the game complexity in real-time, based on simulated student performance. The adaptive dice game provides personalized learning experiences that improve both engagement and comprehension of key mathematical concepts. The experiment evaluates how adaptive difficulty settings influence strategic decision-making and learning outcomes. The results demonstrate that adaptive learning systems can significantly enhance mathematical education by offering customized learning paths that improve understanding of complex concepts. This study contributes to the discussion on the potential of integrating AI with educational technologies to enhance learning outcomes, particularly in disciplines that demand high analytical skills.

Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson's Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. By focusing on a single biomarker, our approach simplifies PD diagnosis, integrates seamlessly into clinical workflows, and provides interpretable, actionable insights. Although DATSCAN has limitations in detecting early-stage PD, our study demonstrates the potential of ML to enhance diagnostic precision, contributing to improved clinical decision-making and patient outcomes.

Metacognitive processes, situational factors, and clinical decision-making in nursing education: a quantitative longitudinal study.

This study examined the longitudinal development of metacognitive skills and clinical decision-making abilities in nursing students, focusing on the interactions between metacognitive processes, situational factors, and individual differences. A longitudinal, quantitative design was employed, following 185 third-year nursing students from a major university in China over one academic year. Data were collected at six time points using the Metacognitive Awareness Inventory, Nursing Decision-Making Instrument, and custom-designed clinical scenario assessments. Latent Growth Curve Modeling, Multilevel Modeling, and Moderation Analysis were used to analyze the data. Significant positive growth trajectories were observed for both metacognitive awareness (mean slope = 0.07, p < .001) and decision-making skills (mean slope = 0.08, p < .001). Metacognitive regulation emerged as the strongest predictor of decision-making outcomes (β = 0.188, p < .001 for quality; β = 0.168, p < .001 for efficiency). Task complexity negatively impacted decision-making quality (β = -0.129, p < .001), while time pressure more strongly affected efficiency (β = -0.121, p < .001). Cognitive style and emotional intelligence moderated the relationship between metacognitive processes and decision-making outcomes. The effectiveness of metacognitive strategies in mitigating the negative effects of situational factors varied across individuals and over time. This study provides robust evidence for the complex interplay between metacognitive processes, situational factors, and individual differences in the development of clinical decision-making skills among nursing students. The findings highlight the importance of tailoring educational interventions to enhance specific metacognitive skills, particularly regulation, while considering the impact of situational factors and individual cognitive styles. These insights can inform the design of more effective, personalized approaches to nursing education, potentially enhancing the preparation of nursing students for the complexities of clinical practice.