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BACKGROUND: Surveys suggest a low level of implementation of clinical guidelines, although they are intended to improve the quality of treatment and patient safety. Which guideline recommendations are not followed and why has yet to be analysed. In this study, we investigate the proportion of European and national guidelines followed in the area of pre-operative anaesthetic evaluation prior to non-cardiac surgery.
METHODS: We conducted this monocentric retrospective observational study at a German university hospital with the help of software that logically links guidelines in such a way that individualised recommendations can be derived from a patient\'s data. We included routine logs of 2003 patients who visited our pre-anaesthesia outpatient clinic between June 2018 and June 2020 and compared the actual conducted pre-operative examinations with the recommendations issued by the software. We descriptively analysed the data for examinations not performed that would have been recommended by the guidelines and examinations that were performed even though they were not covered by a guideline recommendation. The guidelines examined in this study are the 2018 ESAIC guidelines for pre-operative evaluation of adults undergoing elective non-cardiac surgery, the 2014 ESC/ESA guidelines on non-cardiac surgery and the German recommendations on pre-operative evaluation on non-cardiothoracic surgery from the year 2017.
RESULTS: Performed ECG (78.1%) and cardiac stress imaging tests (86.1%) indicated the highest guideline adherence. Greater adherence rates were associated with a higher ASA score (ASA I: 23.7%, ASA II: 41.1%, ASA III: 51.8%, ASA IV: 65.8%, P < 0.001), lower BMI and age > 65 years. Adherence rates in high-risk surgery (60.5%) were greater than in intermediate (46.5%) or low-risk (44.6%) surgery (P < 0.001). 67.2% of technical and laboratory tests performed preoperatively were not covered by a guideline recommendation.
CONCLUSIONS: Guideline adherence in pre-operative evaluation leaves room for improvement. Many performed pre-operative examinations, especially laboratory tests, are not recommended by the guidelines and may cause unnecessary costs. The reasons for guidelines not being followed may be the complexity of guidelines and organisational issues. A software-based decision support tool may be helpful.
BACKGROUND: ClinicalTrials.gov ID NCT04843202.

BACKGROUND: The mechanisms linking neural and behavioral indices of reduced reward sensitivity in depression, particularly in children, remain unclear. Reward positivity (RewP), a neural index of reward processing, has been consistently associated with depression. Separately, recent studies using the drift-diffusion model (DDM) on behavioral data have delineated computational indices of reward sensitivity. Therefore, the present study examined whether RewP is a neural mediator of DDM-based indices of reward processing in predicting pediatric depression across varying levels of symptom severity.
METHODS: A community sample of 166 girls, aged 8 to 14 years, completed two tasks. The first was a reward guessing task from which RewP was computed using electroencephalography; the second was a probabilistic reward-based decision-making task. On this second task, DDM analysis was applied to behavioral data to quantify the efficiency of accumulating reward-related evidence (drift rate) and potential baseline bias (starting point) towards the differently rewarded choices. Depression severity was measured using the self-report Children\'s Depression Inventory (CDI).
RESULTS: RewP was correlated with drift rate, but not starting point bias, towards the more rewarded choice. Furthermore, RewP completely mediated the association between a slower drift rate towards the more rewarded option and higher depression symptom severity.
CONCLUSIONS: Our findings suggest that reduced neural sensitivity to reward feedback might be a neural mechanism underscoring behavioral insensitivity to reward in children and adolescents with higher depression symptom severity, offering novel insights into the relationship between neural and computational indices of reward processing in this context.

OBJECTIVE: This study explored the decision-making experiences of patients and their partners or primary caregiver who opted for experimental active surveillance (instead of standard surgery) for the treatment of esophageal cancer.
METHODS: Seventeen couples participated. Semi-structured interviews were conducted on couples\' joint experiences as well as their individual experiences. Preferred and perceived role in the treatment decision-making process was assessed using the adjusted version of the Control Preferences Scale, and perceived influence on the treatment decision was measured using a visual analog scale.
RESULTS: Couples reflected on the decision-making process as a positive collaboration, where patients retain their autonomy by making the final decision, and partners offer emotional support. Couples reported about an overwhelming amount and sometimes conflicting information about treatments among different hospitals and healthcare providers.
CONCLUSIONS: Patients often involve their partner in decision-making, which they report to have enhanced their ability to cope with the disease. The amount and sometimes conflicting information during the decision-making process provide opportunities for improvement.
CONCLUSIONS: Couples can benefit from an overview of what they can expect during treatment course. If active surveillance becomes an established treatment option in the future, provision of such overviews and consistent information should become more streamlined.

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.

BACKGROUND: Femoral neck fracture (FNF) accounts for approximately 3.58% of all fractures in the entire body, exhibiting an increasing trend each year. According to a survey, in 1990, the total number of hip fractures in men and women worldwide was approximately 338,000 and 917,000, respectively. In China, FNFs account for 48.22% of hip fractures. Currently, many studies have been conducted on postdischarge mortality and mortality risk in patients with FNF. However, there have been no definitive studies on in-hospital mortality or its influencing factors in patients with severe FNF admitted to the intensive care unit.
OBJECTIVE: In this paper, 3 machine learning methods were used to construct a nosocomial death prediction model for patients admitted to intensive care units to assist clinicians in early clinical decision-making.
METHODS: A retrospective analysis was conducted using information of a patient with FNF from the Medical Information Mart for Intensive Care III. After balancing the data set using the Synthetic Minority Oversampling Technique algorithm, patients were randomly separated into a 70% training set and a 30% testing set for the development and validation, respectively, of the prediction model. Random forest, extreme gradient boosting, and backpropagation neural network prediction models were constructed with nosocomial death as the outcome. Model performance was assessed using the area under the receiver operating characteristic curve, accuracy, precision, sensitivity, and specificity. The predictive value of the models was verified in comparison to the traditional logistic model.
RESULTS: A total of 366 patients with FNFs were selected, including 48 cases (13.1%) of in-hospital death. Data from 636 patients were obtained by balancing the data set with the in-hospital death group to survival group as 1:1. The 3 machine learning models exhibited high predictive accuracy, and the area under the receiver operating characteristic curve of the random forest, extreme gradient boosting, and backpropagation neural network were 0.98, 0.97, and 0.95, respectively, all with higher predictive performance than the traditional logistic regression model. Ranking the importance of the feature variables, the top 10 feature variables that were meaningful for predicting the risk of in-hospital death of patients were the Simplified Acute Physiology Score II, lactate, creatinine, gender, vitamin D, calcium, creatine kinase, creatine kinase isoenzyme, white blood cell, and age.
CONCLUSIONS: Death risk assessment models constructed using machine learning have positive significance for predicting the in-hospital mortality of patients with severe disease and provide a valid basis for reducing in-hospital mortality and improving patient prognosis.

UNASSIGNED: The basic product information, health, safety, and nutritional details, as well as food marketing, advertising, and promotion, are all provided on a packed food label. This study was carried out to assess the knowledge, attitude, and practices of the shoppers regarding food labels as there are few studies done in India and none in Pune.
UNASSIGNED: A cross-sectional study was carried out among 226 participants using a validated semi-structured questionnaire, which consisted of sociodemographic details and questions on their knowledge, attitude, and practice regarding food labels while purchasing a packed food item. Quantitative data are presented in the form of mean, standard deviation, 95% confidence interval (CI) of mean, median, and interquartile range (IQR). Qualitative data are in terms of number (N), percentages (%), and 95% CI of percentage.
UNASSIGNED: Of the 226 participants, 163 (72.12) were aware of the label on packaged foods. One hundred seventy-seven (78.32%) participants in all read the label on the packaged food. The maximum retail price (MRP) and expiration date were the most frequent labels sought. Although 17.7% of consumers did not consider nutrient composition when making a purchase, energy and protein were the most often considered nutrients. The majority (70.35%) falsely believed that juice was healthy. The packed food label\'s tiny font made it difficult to read for 30.38% of the customers.
UNASSIGNED: Despite the increased awareness about packed food labeling, a fraction of shoppers were not concerned about nutritional information. In contrast to the prevailing knowledge, the majority believes packed food has a positive effect on health.

Many everyday decisions, including those concerning our health, finances and the environment, involve choosing between a smaller but imminent reward (e.g., €20 now) and a later but larger reward (e.g., €40 in a month). The extent to which an individual prefers smaller imminent rewards over larger delayed rewards can be measured using delay discounting tasks. Acute stress induces a cascade of biological and psychological responses with potential consequences for how individuals think about the future, process rewards, and make decisions, all of which can impact delay discounting. Several studies have shown that individuals focus more on imminent rewards under stress. These findings have been used to explain why individuals make detrimental choices under acute stress. Yet, the evidence linking acute stress to delay discounting is equivocal. To address this uncertainty, we conducted a meta-analysis of 11 studies (14 effects) to systematically quantify the effects of acute stress on monetary delay discounting. Overall, we find no effect of acute stress on delay discounting, compared to control conditions (SMD = -0.18, 95% CI [-0.57, 0.20], p = 0.32). We also find that neither the gender/sex of the participants, the type of stressor (e.g., physical vs. psychosocial) nor whether monetary decisions were hypothetical or incentivized (i.e. monetary decisions were actually paid out) moderated the impact of acute stress on monetary delay discounting. We argue that establishing the effects of acute stress on the separate processes involved in delay discounting, such as reward valuation and prospection, will help to resolve the inconsistencies in the field.

BACKGROUND: Nominal group technique (NGT) is widely used in healthcare research to facilitate decision-making and consensus-building. Traditional NGT requires face-to-face interaction and its limitations include potential biases, logistical challenges and high costs. The COVID-19 pandemic necessitated a shift to virtual methods, which led to the development of virtual nominal group technique (vNGT). Aim To reflect on the use of vNGT, particularly in the context of Ghader et al (2023 ), to understand its effectiveness in overcoming the limitations of traditional NGT and on its applicability in pandemic-affected scenarios.
METHODS: This paper reviews and discusses literature on the use and effectiveness of NGT compared to other consensus-building methods and examines the emergence of vNGT in overcoming the limitations of traditional NGT. The authors also reflect on their use of vNGT in a study to identify cardiovascular research priorities in the UAE and provide details of its implementation.
CONCLUSIONS: vNGT bridges the gap between the localised nature of traditional NGT and the global reach of the Delphi technique. It allows for the inclusion of diverse participants, saves costs and offers time-efficiency. The study demonstrated vNGT\'s adaptability, with participants engaging in idea generation, discussion and prioritisation using online tools. However, challenges persist with vNGT, including reduced accessibility for certain demographics, timing issues across time zones and technical difficulties.
CONCLUSIONS: vNGT successfully integrates the interactive, consensus-building aspects of NGT with the broad reach of Delphi. It can be valuable in research and decision-making, especially in an era of increased remote collaboration.
CONCLUSIONS: vNGT can significantly impact healthcare research and policy formulation by enabling more inclusive, cost-effective and timely consensus-building processes. However, considerations for accessibility and technical support are crucial for its wider adoption and effectiveness.

S/S carriers of 5-HTTLPR have been found to be more risk seeking for losses compared to L/L carriers. This finding may be the result of reduced top-down control from the frontal cortex due to altered signal pathways involving the amygdala and ventral striatum. The serotonergic system is known to be involved in neurodevelopment and neuroplasticity. Therefore, the aim of this study was to investigate whether structural differences in white matter can explain the differences in risk-seeking behaviour. Lower structural connectivity in S/S compared to L/L carriers and a negative relationship between risk seeking for losses and connectivity were assumed. Diffusion-weighted imaging was used to compute diffusion parameters for the frontostriatal and uncinate tract in 175 genotyped individuals. The results showed no significant relationship between diffusion parameters and risk seeking for losses. Furthermore, we did not find significant differences in diffusion parameters of the S/S vs. L/L group. There were only group differences in the frontostriatal tract showing stronger structural connectivity in the S/L group, which is also reflected in the whole brain approach. Therefore, the data do not support the hypothesis that the association between 5-HTTLPR and risk seeking for losses is related to differences in white matter pathways implicated in decision-making.