Dataset Research Articles

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Perception of Graduate Students of Nepal towards Online Shopping

The growing use of internet in Nepal provides a developing prospect for online shopping. This study focuses the variables or the factors on which graduate students keep in mind while shopping through online. This research deals with the perception of graduate students towards online shopping in Nepalese context. The theoretical model of this research was based on perceived usefulness, perceived ease of use, perceived risk and attitude of graduate students towards online shopping respectively. The theoretical model is also related to the online related lifestyle of graduate students. The study adopted the quantitative research design where data were collected through the distributed questionnaire. 300 samples of data were collected using electronic form (Google form). IBM SPSS (International Business Machine Statistical Package for Social Sciences) statistics 24 was used for the analysis of collected data. Descriptive analysis was conducted to describe and understand the feature specific data set where inferential statistics was employed to establish the relationship among the variables and to test the proposed hypothesis. The findings of the study showed that perception and attitude of graduate students towards online shopping is positive. There was also a significant relationship between perceived usefulness and attitude. There was significant relationship between perceived ease of use and attitude. Likewise there was significant relationship between perceived risk and attitude towards online shopping. Finally, the study provides insight for online related businessman and investors for the further development of online business.

Predictors of response to Radioactive Iodine Therapy in Intermediate and high risk patients with papillary thyroid carcinoma

BackgroundRadioactive iodine (RAI) therapy is the standard treatment approach after total thyroidectomy in patients with papillary thyroid carcinoma (PTC). We aimed to identify predictive factors of response to the treatment in intermediate and high-risk patients with PTC. In addition, the impact of multiple RAI treatments was explored.MethodsIn a 3-year retrospective study, data from intermediate and high-risk patients with PTC who received RAI therapy following total thyroidectomy, were analyzed by the end of year-one and year-three. Demographic data, tumor size, capsular/vascular invasion, extrathyroidal extension, local or distant metastasis, initial dose and cumulative dose of RAI, serum thyroglobulin(Tg), antithyroglobulin antibody(TgAb), and imaging findings were investigated. Patients with an excellent response to a single dose of RAI treatment, after three years of follow-up were classified as the “Responder group”. Excellent response was defined as stimulated serum Tg less than 1 ng/ml, or unstimulated serum Tg less than 0.2 ng/ml in TgAb-negative patients with negative imaging scans.Results333 patient records with a complete data set were analyzed in this study. After three years of initial treatment, 271 patients were non-responders (NR) and 62 were responders (R). At baseline, the median pre-ablation serum Tg level was 5.7 ng/ml in the NR group, and 1.25 ng/ml in the R group (P < 0.001). TSH-Stimulated serum Tg greater than 15.7 ng/ml, was associated with response failure even after multiple RAI therapy, AUC: 0.717(0.660–0.774), sensitivity: 52.5%, specificity: 89.47%, P < 0.001. On the other hand, multiple RAI therapy was associated with excellent response in 16.2% of the patients. The chance of ER was decreased by 74% if initial post-operation ultrasound imaging confirmed the presence of locoregional involvement, OR 0.26, (95% CI: 0.12–0.55), P < 0.001.ConclusionStimulated serum Tg and locoregional involvement after total thyroidectomy are predictive factors of non-response to RAI therapy in intermediate and high-risk patients with PTC. In addition, a minority of patients achieve excellent response after multiple RAI therapy.

Proposing Multiregional Diagnostic Reference Levels for Common CT Angiography Examinations in Saudi Arabia

Objectives: Diagnostic reference levels (DRLs) are crucial tools for optimizing radiation exposure during different radiological examinations. This study aimed to establish preliminary DRLs for commonly performed computed tomographic angiography (CTA) examinations in Saudi Arabia. Methods: Data for three types of CTA examinations (cerebral, pulmonary, and lower-extremity) were collected from six medical cities across Saudi Arabia. Data sets related to 723 CTAs with a mean patient weight of 75 kg were analysed in detail. The DRL values were determined based on the 75th, median, and 25th CT dose index volume (CTDIvol) and dose length product (DLP) values. Results: The established DRLs were 1221 mGy cm for cerebral CTAs, 475 mGy cm for pulmonary CTAs, and 1040 mGy cm for lower-extremity CTAs. These values were comparable to those reported in other studies. Conclusions: This study provides preliminary DRLs for three common CTA procedures in Saudi Arabia. The widespread implementation of a low kVp and a high level of image reconstruction (IR) presents an opportunity for further dose reduction. These findings can serve as a foundation for future nationwide DRL surveys and the optimization of CTA imaging protocols in Saudi Arabia.

Plasma proteins associated with plant-based diets: Results from the Atherosclerosis Risk in Communities (ARIC) study and Framingham Heart Study (FHS)

Background & AimsPlant-based diets are associated with a lower risk of chronic diseases. Large-scale proteomics can identify objective biomarkers of plant-based diets, and can improve our understanding of the pathways that link plant-based diets to health outcomes. This study investigated the plasma proteome of four different plant-based diets [overall plant-based diet (PDI), provegetarian diet, healthful plant-based diet (hPDI), and unhealthful plant-based diet (uPDI)] in the Atherosclerosis Risk in Communities (ARIC) Study and replicated the findings in the Framingham Heart Study (FHS) Offspring cohort. MethodsARIC Study participants at visit 3 (1993-1995) with completed food frequency questionnaire (FFQ) data and proteomics data were divided into internal discovery (n=7,690) and replication (n=2,543) data sets. Multivariable linear regression was used to examine associations between plant-based diet indices (PDIs) and 4,955 individual proteins in the discovery sample. Then, proteins that were internally replicated in the ARIC Study were tested for external replication in FHS (n=1,358). Pathway overrepresentation analysis was conducted for diet-related proteins. C-statistics were used to predict if the proteins improved prediction of plant-based diet indices beyond participant characteristics. ResultsIn ARIC discovery, a total of 837 diet-protein associations (PDI=233; provegetarian=182; hPDI=406; uPDI=16) were observed at false discovery rate (FDR) <0.05. Of these, 453 diet-protein associations (PDI=132; provegetarian=104; hPDI=208; uPDI=13) were internally replicated. In FHS, 167/453 diet-protein associations were available for external replication, of which 8 proteins (PDI=1; provegetarian=0; hPDI=8; uPDI=0) replicated. Complement and coagulation cascades, cell adhesion molecules, and retinol metabolism were over-represented. C-C motif chemokine 25 for PDI and 8 proteins for hPDI modestly but significantly improved the prediction of these indices individually and collectively (P value for difference in C-statistics<0.05 for all tests) ConclusionsUsing large-scale proteomics, we identified potential candidate biomarkers of plant-based diets, and pathways that may partially explain the associations between plant-based diets and chronic conditions.

How does emotional intelligence develop in early childhood?: a meta-analysis of two perspectives on emotional intelligence

ABSTRACT This study aims to identify the theoretical premises and key variables affect to develop children’s emotional intelligence (EI). To this end, the Preferred Studies for Systematic Reviews and Meta-Analysis (PRISMA) was used for the meta-analysis. The final data set consisted of 73 cases which affect to develop children’s EI from 24 studies, and the data set was divided as Ability-EI/Trait-EI group by theoretical perspectives. The results are as follows: First, all 73 variables were found to affect the development of children’s EI in general. Second, in the Ability-EI group, the result proved that the parental emotional expression plays an important role in children’s development of EI. In the Trait-EI group, teacher’s skills and self-efficacy were crucial variables to develop children’s EI. The results above imply the need for multifaceted interpretations of children’s EI and emphasize the importance of parents and teachers in the environments to foster children’s EI.

Live fitting of process data within digital twins of manufacturing to use simulation and optimisation

ABSTRACT In production scenarios, uncertainty in production times, and scrap rates is common. Uncertainty can be described by stochastic models that need continuous updates due to changing conditions. This paper models probability distributions and estimates their parameters from real-world data on processing times and scrap rates. It uses live fitting to identify timely changes in the data sets, comparing different distributions. The fitting and live fitting approaches are applied to data from a real production system to compare the goodness of fit for different distributions and to demonstrate the reliability of the reaction to changes in the input data. Data and probability estimations are categorised, and a concept combining simulation and optimisation models is developed to optimise scheduling. The vision of the research presented in this paper is an online approach that determines quasi-optimal schedules for production systems based on current data from the system and its environment.

Cross Country Study of Human Capital Formation and USFDI Inflows

Purpose: The neo-classical theory of economic growth features the positive impact of the human capital of an economy on its growth. The inflow of foreign direct investment is one of the main components to carry forward the growth effect, this study investigates the impact of country-specific institutional quality and human capital on the cross-country variations of US-Foreign Direct Investment (FDI) inflows. Our core hypothesis is that Countries with better Human capital will attract more FDI if they have good quality institutions in terms of less risk in investment opportunities. Method: Using a set of panel data of US-Foreign Direct Investment (FDI) inflows for both developed and developing countries over the period 1984-2021. Result: The country-specific quality of institutions reinforces the impact of human capital on the inflow of US foreign direct investment in this study. Using two-dimensional panel data we find strong support for our hypothesis using a two-way fixed effects model. Our results are robust to the alternative measures of institutional quality. Implications: The strength of our approach is that in contrast to the previous works (1) we have used the USFDI inflow across countries that have been widely neglected in the related literature and (2) emphasis has been given to the conditional or joint impact of human capital and country-specific institutional quality in determining the cross-country variations in USFDI inflows.

NQuack: An R package for predicting ploidal level from sequence data using site‐based heterozygosity

AbstractPremiseTraditional methods of ploidal‐level estimation are tedious; using DNA sequence data for cytotype estimation is an ideal alternative. Multiple statistical approaches to leverage sequence data for ploidy inference based on site‐based heterozygosity have been developed. However, these approaches may require high‐coverage sequence data, use inappropriate probability distributions, or have additional statistical shortcomings that limit inference abilities. We introduce nQuack, an open‐source R package that addresses the main shortcomings of current methods.Methods and ResultsnQuack performs model selection for improved ploidy predictions. Here, we implement expectation maximization algorithms with normal, beta, and beta‐binomial distributions. Using extensive computer simulations that account for variability in sequencing depth, as well as real data sets, we demonstrate the utility and limitations of nQuack.ConclusionsInferring ploidy based on site‐based heterozygosity alone is difficult. Even though nQuack is more accurate than similar methods, we suggest caution when relying on any site‐based heterozygosity method to infer ploidy.

Improving Shear Strength Prediction in Steel Fiber Reinforced Concrete Beams: Stacked Ensemble Machine Learning Modeling and Practical Applications

Existing machine learning (ML) models often encounter challenges in accurately predicting the shear strength of steel fiber reinforced concrete (SFRC) beams, mainly due to a lack of generalization. This study introduces an advanced stacked ensemble ML architecture to overcome this limitation by utilizing a comprehensive data set of 394 experimental observations and a 20-feature matrix. The model exhibits exceptional performance with a mean absolute error of 0.391 and a correlation coefficient (R2) of 93.7%, and surpasses traditional ML algorithms. Furthermore, a sensitivity analysis of the developed model yields that shear strength is highly responsive to the shear span-to-effective depth ratio, with an increase from 1 to 4 resulting in a significant reduction (about 50%) in strength. Increasing the percentage of longitudinal steel from 1 to 2% leads to a 14.6% gain, whereas doubling its yield strength has a more modest 3.7% effect. Increasing the compressive strength of concrete from 25 to 50 MPa, notably increases the shear strength by 19.6%. Fiber length, diameter, and aspect ratio exhibit varying impacts, with shear strength most influenced by the fiber volume fraction, which leads to a peak enhancement of 30.7% at 2% fibrous volume; however, the tensile strength of fibers minimally affects the shear strength. Additionally, this research presents a simplified empirical model to predict the shear strength of SFRC beams based on the key determinants. This model employs the iterative Gauss–Newton algorithm, demonstrates reasonable predictive capability, and boasts an R2 of 83.3% and mean prediction-tested strengths of around 1.039. The practical implications of these findings are substantial for the construction industry as they enable a more accurate and reliable design of SFRC beams, optimize material usage, and potentially reduce construction costs as well as enhance structural safety.

A new sine-inspired probability model: Theoretical features with statistical modeling of the music engineering and reliability scenarios

It is a proven authenticity that probability-schooled methodologies have a cardinal role in making decisions under the vagueness circumstances. Thus, to reach meaningful and fruitful decisions under vagueness circumstances, numerous probability-focused methodologies have been introduced, promoted, and implemented. This paper also contributes to adding another useful probability model to the existing literature for data modeling in engineering-oriented areas. The new model is called the weighted sine exponentiated exponential (WSEE) distribution, which is formed by implementing the exponentiated exponential model together with a sine-adapted method. Certain properties of the WSEE distribution are given. The parameter estimation of the WSEE distribution along with the simulation study is also provided. Last, in order, we apply the WSEE distribution for analyzing three data sets taken from the musical engineering and reliability settings. The optimality of the WSEE distribution is proved by comparing its results with other prominent modifications of the exponentiated exponential distribution. Using 4 statistical criteria as judgment tools, it was shown numerically and pictorially that the WSEE distribution is an optimal and suitable model for the musical engineering reliability data sets.

Development of embedded-atom method (EAM) potential for Palladium–Barium alloy

ABSTRACT An embedded-atom method (EAM) potential for the Pd–Ba alloy system has been developed in order to forward computational research in this alloying system as there is no EAM potential available for this alloy system. The force-matching method has been implemented to develop the EAM potential first, and then, optimisation to converged density-functional theory (DFT) data sets has been done to generate the accurate and reliable potential for the Pd–Ba alloy system. Some physical, elastic and thermal properties of BaPd2 crystal have been calculated through molecular dynamics (MD) simulation using the developed EAM potential and then verified these properties with the help of DFT analysis in order to examine the performance of the potential. The presence of some even peaks of BaPd2 in virtual XRD spectra using MD simulation has been justified by DFT analysis. Slight deviations in melting points calculation at different compositions of the Pd–Ba alloy system have been observed. Higher Ba–Pd interaction using radial distribution characteristics and slower kinetics for inter-diffusion through diffusional characteristics study of BaPd2 have been reported using MD simulation with the developed EAM potential. In spite of some discrepancies due to deficiency in the potential, a closer agreement between MD and DFT analysis has been observed.

Ranking of Different States in India Based on Sustainable Women Empowerment Using MCDM Methodology Under Uncertain Environment

In this paper, women’s empowerment in different states of India is considered. Based on considered data sets, the states of India are ranked with Decision-Making (MCDM) methodology. Here, Generalized Triangular Intuitionistic Fuzzy Numbers (GTIFNs) are considered. Here, GTIFNs are taken to deal with the uncertainty and we introduce a new de-fuzzification method for converting the GTIFNs to corresponding crisp values. Here, we have applied two MCDM techniques namely the Entropy-weighted method and Vlekriterijumsko KOmpromisno Rangiranje (VIKOR) method. The entropy-weighted method is used for evaluating the criteria weights and the VIKOR method is applied to rank the alternatives. Last, to verify the stability and vagueness of the system, we perform sensitivity and comparative analysis.

Critical Drop Height Prediction of Loquat Fruit Based on Some Engineering Properties with Machine Learning Approach

The lowest height at which a product can fall without suffering severe harm is known as the “critical drop height” for agricultural products. It is a crucial factor to take into account for crops like loquats that are prone to bruising or damage upon impact. By establishing the minimum altitude at which the product can be dropped without experiencing substantial harm, suitable processing procedures may be established from harvest to the end consumer, thereby preserving product quality and worth. The critical drop height can be ascertained through swift, affordable, non-destructive, and non-traditional methods, rather than time-consuming and expensive laboratory trials. In the study, we aimed to estimate the critical drop height for loquat fruit using machine learning methods. Three different machine learning methods with different operating principles were applied. R2, MAE, RMSE, and MAPE metrics were used to assess the models. There were no obvious differences in both the comparisons within the models, namely the training and test results and the mutual comparisons of the models. However, with a slight difference, the SVMs model performed better in the training data set, and the ETs model performed better in the test data set. Plots were drawn to visualize model performances, and the results obtained from the plots and metrics support each other.

Optimizing feedforward neural networks using a modified weighted mean of vectors: Case study chemical datasets

This paper proposes a modified version of the weighted mean of vectors algorithm (mINFO), which combines the strengths of the INFO algorithm with the Enhanced Solution Quality Operator (ESQ). The ESQ boosts the quality of the solutions by avoiding optimal local values, verifying that each solution moves towards a better position, and increasing the convergence speed. Furthermore, we employ the mINFO algorithm to optimize the connection weights and biases of feedforward neural networks (FNNs) to improve their accuracy. The efficacy of FNNs for classification tasks is mainly dependent on hyperparameter tuning, such as the number of layers and nodes. The mINFO was evaluated using the IEEE Congress on Evolutionary Computation held in 2020 (CEC’2020) for optimization tests, and ten chemical data sets were applied to validate the performance of the FNNs classifier. The proposed algorithm’s results have been evaluated with those of other well-known optimization methods, including Runge Kutta optimizer’s (RUN), particle swarm optimization (PSO), grey wolf optimization (GWO), Harris hawks optimization (HHO), whale optimization algorithm (WOA), slime mould algorithm (SMA) and the standard weighted mean of vectors (INFO). In addition, some improved metaheuristic algorithms. The experimental results indicate that the proposed mINFO algorithm can improve the convergence speed and generate effective search results without increasing computational costs. In addition, it has improved the FNN’s classification efficiency.

Needle tracking in low-resolution ultrasound volumes using deep learning.

Clinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes. We design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 16 element matrix transducer with a volume rate of 4Hz. We compare the performance of our deep learning approach with conventional needle segmentation. Our experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54mm in water and 1.54mm in liver for deep learning. Our study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.

Long-term safety of enzyme replacement therapy with agalsidase alfa in patients with Fabry disease: post-marketing extension surveillance in Japan

Fabry disease is a rare inherited X-linked metabolic disorder in which deficient alpha-galactosidase A activity causes progressive build-up of globotriaosylceramide (Gb3) and multi-system dysfunction. Following approval of agalsidase alfa for Fabry disease in Japan in 2006, an 8-year all-case post-marketing surveillance (PMS) showed that the treatment was well tolerated and effective for managing disease progression in adult Japanese patients. The present nationwide prospective observational study extended the initial PMS by enrolling patients who continued agalsidase alfa treatment after the initial 8-year period in a 6.5-year extension survey. Patient information from the initial PMS and the extension survey was evaluated as a single data set (observation period: February 2007–September 2021). Of 493 patients in the initial PMS, 129 (45.0% male classic, 6.2% male non-classic, 48.8% female heterozygous phenotype) consented to participate in the extension survey and were included in the analysis. The mean duration of treatment was 9.6 years. A total of 145 adverse drug reactions (ADRs) occurred in 31 patients (24%), and 22 serious ADRs occurred in 12 patients (9.3%). Although serious cardiac, renal, or cerebrovascular adverse events decreased in frequency over time in male patients, serious cardiac events continued to occur in female patients, who showed higher incidence of cardiac complications at baseline. No new safety concerns were identified. Additionally, long-term agalsidase alfa treatment sustained the initial reduction in Gb3 concentrations without increasing the rate of anti-agalsidase antibody positivity. These findings suggest that agalsidase alfa treatment demonstrates continued safety and sustains patients' clinical course over the long term.

Money laundering through video games, a criminals' playground

Money laundering and video games provide opportunities to criminals for easier and less detectable methods of performing money laundering. These actions may be used as part of a system of transactions, by these criminals, to further disguise the origins of their funds. The use of videogames as a tool to launder money is something that has been only briefly explored. This work identifies the ways that money laundering through video game secondary marketplaces can offer benefits to criminals looking to launder money, versus the use of traditional money laundering methods.We explore the potential for using publicly accessible data, such as that available from the Steam Marketplace, to identify suspicious transactions that may indicate the existence of money laundering within these platforms. This research focused on identifying irregularities in the frequency and quantity of trades on the Steam Marketplace.The results of this investigation show that identifying, using very simple money laundering detection methods, possible cases of money laundering within transactional data from the Steam Marketplace is possible. The data used shows that there were several suspicious transactions and accounts which could warrant further investigation, and may be involved in activity which represents money laundering. As a result of this, there is scope for further investigations using larger data sets and examination of other publicly accessible data using a greater range of methods to identify suspicious transactions including, but not limited to, value of transactions and location.

Two-tier architecture-based grading for pancreatic cancer outperforms traditional grading.

A novel architecture-based grading system for pancreatic ductal adenocarcinoma (PDAC) is tested against traditional grading. A total of 103 PDAC resections were graded by College of American Pathologists/American Joint Committee on Cancer (CAP/AJCC) guidelines and by a system using an architectural pattern (dispersed larger duct = low grade vs dense smaller duct = high grade). Survival analyses and interobserver variability were assessed. In total, 114 cases from a public data set were used for validation. Median overall survivals were 15 and 36 months for architectural high-grade and low-grade cases, respectively (P<.001). Conversely, CAP/AJCC grading showed no survival difference between well-differentiated and moderately differentiated tumors (P=.545). Architecture-based grading remained prognostically significant for recurrence-free survival (P=.004), but CAP/AJCC grading was not (P=.226). Adjusted for stage and margin status, architectural high-grade PDACs showed a hazard ratio of 2.69 relative to low grade (P<.001) for survival. The validation cohort confirmed prognostic differences in overall (P<.001) and recurrence-free survival (P=.027) for the architecture-based system, outperforming CAP/AJCC grading. Architecture-based grading exhibited a Cohen's ĸ value of 0.710 (substantial agreement), superior to traditional grading (0.488, moderate agreement). Grading PDAC based on architectural pattern results in superior prognostication and reproducibility vs CAP/AJCC grading.

Observational study of missing SOFA score data frequency in RCTs relative to ICU length of stay

The Sequential Organ Failure Assessment, also known as SOFA score, was introduced to assess organ dysfunction of critical ill patients. However, understanding the impact of missing SOFA scores in randomized controlled trials and how this affect the validity and applicability of the SOFA score as a surrogate endpoint for predicting mortality has been a matter of interest. To address this, a secondary analysis of a systematic review was conducted to quantify the relationship between SOFA scores and the prediction of mortality in critically ill adults in randomized controlled trials (RCTs). The systematic review being referred to included 87 RCTs with a total of 12,064 critically ill patients. This analysis focused on missing SOFA score data in relation to the length of stay on the intensive care unit (ICU) and the methods used to handle missing data. SOFA score measurements from the included studies were categorized into three time frames: Early (t ≤ 4 days), Intermediate (t = 5–10 days) and Late (t > 10 days) measurement. Only one study reported a complete data set for calculating the SOFA score for an Early measurement. When considering all methods used to address missing data, 32% of studies still had missing data for Early measurements, and this percentage increased to 64% for Late measurements. These findings suggested that, over time, the number of studies with incomplete data sets has been increasing. The longer a patient is treated on the ICU, the higher the number of missing data which can impact the validity of SOFA score analyses. There was no clear trend towards a specific method for compensating missing data. An exemplary calculation demonstrated that ignoring missing data may lead to an underestimated variability of the treatment effect. This, in turn, could bias the interpretation of study results by policy- and clinical decision-makers. Overall, there are several limitations that need to be considered when using SOFA score as a surrogate endpoint for mortality. When employed as an outcome, the SOFA score is frequently missing and most studies do not adequately describe the amount or nature of missing data, or the methods used to handle missing data in the analysis.