Online Calculator Research Articles

Machine learning-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients.

Objective: To develop a risk score model for the occurrence of composite cardiovascular events (CVE) in patients with stable angina pectoris (SA) combined with coronary heart disease (CHD) by comparing the modeling effects of various machine learning (ML) algorithms. Methods: In this prospective study, 690 patients with SA combined with CHD attending the Department of Integrative Cardiology, China-Japan Friendship Hospital, from October 2020 to October 2021 were included. The data set was randomly divided into a training group and a testing group in a 7:3 ratio in the per-protocol set (PPS). Model variables were screened using the least absolute shrinkage selection operator (LASSO) regression, univariate analysis, and multifactor logistic regression. Then, nine ML algorithms are integrated to build the model and compare the model effects. Individualized risk assessment was performed using the SHapley Additive exPlanation (SHAP) and nomograms, respectively. The model discrimination was evaluated by receiver operating characteristic curve (ROC), the calibration ability of the model was evaluated by calibration plot, and the clinical applicability of the model was evaluated by decision curve analysis (DCA). This study was approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital (2020-114-K73). Results: 690 patients were eligible to finish the complete follow-up in the PPS. After LASSO screening and multifactorial logistic regression analysis, physical activity level, taking antiplatelets, Traditional Chinese medicine treatment, Gensini score, Seattle Angina Questionnaire (SAQ)-exercise capacity score, and SAQ-anginal stability score were found to be predictors of the occurrence of CVE. The above predictors are modeled, and a comprehensive comparison of the modeling effectiveness of multiple ML algorithms is performed. The results show that the Light Gradient Boosting Machine (LightGBM) model is the best model, with an area under the curve (AUC) of 0.95 (95% CI = 0.91-1.00) for the test set, Accuracy: 0.90, Sensitivity: 0.87, and Specificity: 0.96. Interpretation of the model using SHAP highlighted the Gensini score as the most important predictor. Based on the multifactorial logistic regression modeling, a nomogram, and online calculators have been developed for clinical applications. Conclusion: We developed the LightGBM optimization model and the multifactor logistic regression model, respectively. The model is interpreted using SHAP and nomogram. This provides an option for early prediction of CVE in patients with SA combined with CHD.

The Effect of Cardinal Traits on Compulsive Buying on Zara Product Purchases in Jakarta-Bandung

This study aims to examine the effect of cardinal traits on compulsive buying mediated by hedonistic shopping. The research objects set in this study are women and men aged 18-60 years who have bought and used Zara brand clothing products in Jakarta and Bandung. The research methodology used was quantitative methodology, by making Likert scale questionnaires, in the number of research respondents as many as 125 people. This research phase is a classical assumption test, multiple regression, hypothesis test, and sobel test using an online calculator from Preacher with the help of statistical program of social science (SPSS) version 26. The results showed hedonistic shopping that successfully mediated two cardinal traits variables, including extraversion, and openness to compulsive buying, it is hoped that this study will gain knowledge about strategies that must be done to regulate and assess compulsive buying

Construction and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Non-Dialysis Chronic Kidney Disease Patient.

The aims of this study are to explore the factors affecting mild cognitive impairment in patients with chronic kidney disease (CKD) who are not undergoing dialysis and to construct and validate a nomogram risk prediction model. Using a convenience sampling method, 383 non-dialysis CKD patients from two tertiary hospitals in Chengdu were selected between February 2023 and August 2023 to form the modeling group. The patients were divided into a mild cognitive impairment group (n = 192) and a non-mild cognitive impairment group (n = 191), and factors such as demographics, disease data, and sleep disorders were compared between the two groups. Univariate and multivariate binary logistic regression analyses were used to identify independent influencing factors, followed by collinearity testing, and construction of the regression model. The final risk prediction model was presented through a nomogram and an online calculator, with internal validation using Bootstrap sampling. For external validation, 137 non-dialysis CKD patients from another tertiary hospital in Chengdu were selected between October 2023 and December 2023. In the modeling group, 192 (50.1%) of the non-dialysis CKD patients developed mild cognitive impairment, and in the validation group, 56 (40.9%) patients developed mild cognitive impairment, totaling 248 (47.7%) of all sampled non-dialysis CKD patients. Age, educational level, Occupation status, Use of smartphone, sleep disorders, hemoglobin, and platelet count were independent factors influencing the occurrence of mild cognitive impairment in non-dialysis CKD patients (all p < 0.05). The model evaluation showed an area under the ROC curve of 0.928, 95% CI (0.902, 0.953) in the modeling group, and 0.897, 95% CI (0.844, 0.950) in the validation group. The model's Youden index was 0.707, with an optimal cutoff value of 0.494, sensitivity of 0.853, and specificity of 0.854, indicating good predictive performance; calibration curves, Hosmer-Lemeshow test, and clinical decision curves indicated good calibration and clinical benefit. Internal validation results showed a consistency index (C-index) of 0.928, 95% CI (0.902, 0.953). The risk prediction model developed in this study shows excellent performance, demonstrating significant predictive potential for early screening of mild cognitive impairment in non-dialysis CKD patients. The application of this model will provide a reference for healthcare professionals, helping them formulate more targeted intervention strategies to optimize patient treatment and management outcomes.

The relationship between advanced lung cancer inflammation index and high SYNTAX score in patients with non-ST-elevation myocardial infarction.

The advanced lung cancer inflammation index (ALI) is an independent prognostic biomarker of inflammation and nutrition in various types of cancer, acute heart failure and acute coronary syndrome. The SYNTAX (Synergy between Percutaneous Coronary Intervention with TAXUS and Cardiac Surgery) score (SXscore) is an angiographic scoring tool used to determine the extent and severity of coronary artery disease. To investigate the relationship between ALI and coronary artery lesion complexity assessed using the SXscore in patients with non-ST-segment elevation myocardial infarction (NSTEMI). Between February and November 2020, a total of 284 patients with NSTEMI were included consecutively. ALI was calculated with the formula body mass index (BMI) × serum albumin concentration/neutrophil-to-lymphocyte ratio (NLR). SXscore was calculated using the online calculator and divided into two groups - low (< 32) and high (≥ 33) - and then analyzed. Patients with a high SXscore had lower ALI (22.4 ±7.3 vs. 58.5 ±44.3, p = 0.016). In the univariable analysis, age (p = 0.046), BMI (p = 0.021), C-reactive protein (p = 0.002), peak troponin I (p = 0.009), NLR (p = 0.025), serum albumin (p = 0.003) and ALI (p < 0.001) were significantly associated with a high SXscore. ALI emerged as an independent predictor of a high SXscore in multivariable analysis (95% CI: 0.931-0.984, p = 0.002). The ALI may be useful as a simple tool for predicting high SXscore in patients with NSTEMI. To our knowledge, this is the first study to examine the relationship between ALI and severity of CAD.

Potential value of serum prealbumin and serum albumin in the identification of postoperative delirium in patients undergoing knee/hip replacement: an observational study and internal validation study.

Postoperative delirium (POD) is a common postoperative neurological complication that can lead to a variety of postoperative complications. At present, the pathogenesis of POD is unclear. This study aims to explore the relationship between serum prealbumin and serum albumin and POD and whether serum prealbumin and serum albumin influence POD through POD core pathology. We enrolled 500 Chinese Han patients between September 2020 to January 2023. We analyzed the risk and protective factors of POD using the multivariate logistic regression. We also assessed the predictive power of serum prealbumin, serum albumin, and both in combination with CSF POD biomarkers. We used Stata MP16.0. to examine whether the association between serum prealbumin and serum albumin and POD was mediated by CSF POD biomarkers, and conducted an internal validation study to verify the accuracy of the combination of serum prealbumin + serum albumin + CSF POD biomarkers for predicting POD. The model was visualized using ROC curve and decision curve analysis (DCA). DynNom and Shiny packages were used to create an online calculator. Ten patients who had POD occurring from February 2023 to October 2023 were selected for internal verification. Finally, a total of 364 patients were included in our study. Levels of serum prealbumin, serum albumin in the POD group were lower than those in the NPOD group. The lever of serum prealbumin, serum albumin were protective factors for POD. The relationship between serum prealbumin, serum albumin and POD was partially mediated by T-tau (12.28%) and P-tau (20.61%). The model combining serum prealbumin and serum albumin and POD biomarkers exhibited a relatively better discriminatory ability to predict POD. DCA also showed that the combination of serum prealbumin and serum albumin and POD biomarkers brought high predictive benefits to patients. The dynamic online calculator can accurately predict the occurrence of POD in the internal validation study. Preoperative low serum prealbumin and serum albumin levels were the preoperative risk factors for POD, which is partly mediated by T-tau and P-tau. The model combining serum prealbumin and serum albumin and CSF POD biomarkers can accurately predict the occurrence of POD. http://www.clinicaltrials.gov, identifier ChiCTR2000033439.

Understanding Sample Size Determination In Research: A Practical Guide.

Sample size determination is a critical aspect of business research, influencing the validity, reliability, and generalizability of study findings. This paper provides a comprehensive overview of sample size determination in business research, addressing its significance, statistical foundations, methods, considerations, practical guidelines, and future research directions. Key findings highlight the importance of aligning sample size with research objectives, ensuring adequate statistical power, and considering practical constraints in sample size determination. Various methods, including power analysis, formula-based approaches, and online calculators, are discussed for calculating sample sizes, each with its advantages and considerations. Unique challenges in business research, such as population heterogeneity, sampling techniques, and practical constraints, are explored, along with strategies for addressing these challenges. Practical guidelines and recommendations are provided to assist researchers in determining appropriate sample sizes and improving the reliability and validity of research outcomes. Furthermore, future research directions are identified, including methodological advancements, integration of big data analytics, cross-cultural perspectives, ethical considerations, interdisciplinary collaborations, and validation studies. By addressing these research directions, researchers can advance the field of sample size determination in business research, enhance methodological rigor, and contribute to the generation of high-quality and impactful research outcomes.

Pediatric Critical Care Illness Severity Toolkit: Stata Commands for Calculation of Pediatric Index of Mortality and Pediatric Logistic Organ Dysfunction Scores.

Illness severity scoring tools, such as PRISM III/IV, PIM-3, and PELOD-2, are widely used in pediatric critical care research. However, their application is hindered by complex calculation processes, privacy concerns with third-party online calculators, and challenges in accurate implementation within statistical packages. We have developed a comprehensive, open-source toolkit for implementing the PIM-3, Simplified PIM-3, and PELOD-2 scores. The toolkit includes the pim3 and pelod2 commands and is compatible with Stata versions 12 and above. It features robust data validation, error messaging, a graphical interface, and support for SI and Imperial units. The toolkit's accuracy was validated through unit testing and synthetic data, comparing results with existing implementations. In performance tests, the toolkit exhibited a median processing time of 21.82 seconds for PELOD-2, 14.06 seconds for PIM-3, and 9.74 seconds for Simplified PIM-3, when applied to datasets of 10,000,000 records. It consistently achieved 100% accuracy in both synthetic data tests and manual spot checks. The toolkit decreases processing time and improves accuracy in calculating pediatric critical care severity scores such as PELOD-2, PIM-3, and Simplified PIM-3. Its application in large datasets and validation highlights its utility as a tool for streamlining pediatric critical care research.

Экспериментальное обоснование метода оценки полулетальной дозы облучения для наземного моллюска Fruticicola fruticum M.

There are currently no recommendations in international guidelines (in particular, in ICRP publications) regarding the determination of the medial lethal radiation dose for representatives of “non-human biota”. The use of different methods (algorithms) for calculating an indicator with the same input parameters gives results that differ from each other, sometimes by orders of magnitude. Based on this, the purpose of the study is to calculate the median lethal dose of gamma irradiation of the terrestrial mollusk Fruticicola fruticum M. of two age groups using different tools to justify the most adequate method of assessing LD50. Three algorithms were used for the calculation: the computational method of probit analysis, the capabilities of the R computing environment and the ICARDA online calculator. The input parameters for the calculation were the results of an experiment on acute gamma irradiation of a mollusk with doses of 10-300 Gy. The mortality rate of sexually mature mollusks of two age groups after gamma irradiation was quantified. The duration of the experiment was 210 days, the indicator was recorded every 30 days. The highest LD50 values were obtained when evaluating the indicator by the least squares method using the R software environment, the lowest by the probit analysis method. The calculation of LD50/30 is possible only with errors of the order of 100% or more, since the mortality rate reaches 50% 60 days after irradiation. LD50 for terrestrial mollusks decreases every 30 days after irradiation in increments of 1.3-1.7 times (second group) and 1.2-1.4 times (third group). In the time immediately after irradiation of mollusks, all three calculation algorithms used give identical values of the studied indicator, which is why they can be equally used to estimate LD50. The LD50 for longer periods after irradiation differs depending on the calculation method used: about 50 Gy and 25-30 Gy for the second and third age groups, respectively. At the same time, the results of calculating LD50 using the R and ICARDA programs are almost identical and can be recommended for use, unlike the probit analysis, which leads to underestimated values.

Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning.

Although adult spinal deformity (ASD) surgery aims to restore and maintain alignment, proximal junctional kyphosis (PJK) may occur. While existing scoring systems predict PJK, they predominantly offer a generalized 3-tier risk classification, limiting their utility for nuanced treatment decisions. This study seeks to establish a personalized risk calculator for PJK, aiming to enhance treatment planning precision. Patient data for ASD were sourced from the Korean spinal deformity database. PJK was defined a proximal junctional angle (PJA) of ≥ 20° at the final follow-up, or an increase in PJA of ≥ 10° compared to the preoperative values. Multivariable analysis was performed to identify independent variables. Subsequently, 5 machine learning models were created to predict individualized PJK risk post-ASD surgery. The most efficacious model was deployed as an online and interactive calculator. From a pool of 201 patients, 49 (24.4%) exhibited PJK during the follow-up period. Through multivariable analysis, postoperative PJA, body mass index, and deformity type emerged as independent predictors for PJK. When testing machine learning models using study results and previously reported variables as hyperparameters, the random forest model exhibited the highest accuracy, reaching 83%, with an area under the receiver operating characteristics curve of 0.76. This model has been launched as a freely accessible tool at: (https://snuspine.shinyapps.io/PJKafterASD/). An online calculator, founded on the random forest model, has been developed to gauge the risk of PJK following ASD surgery. This may be a useful clinical tool for surgeons, allowing them to better predict PJK probabilities and refine subsequent therapeutic strategies.

Evaluation of the Performance of the Equations of State Available in CFTurbo for Air

In this publication, we evaluate the performance of equations of state Perfect Gas, Redlich-Kwong, Aungier/ Redlich-Kwong, Soave/ Redlich-Kwong, Peng-Robinson, and Cool Prop in calculating the properties of air at temperatures between 250 K to 800_K and pressure between 1 bar to 250 bar. Firstly, we research the available literature to find the real density (or molar volume or specific volume) at each point. Then we create these equations and use the temperature and density (or molar volume or specific volume) to calculate the pressure, which is then compared to the pressure of the real values. This comparison evaluates the performance of each equation. Finally, we compare our calculations with online calculators to verify our results and comment on the performance of each equation.

PSYCHOSOMATIC MANIFESTATIONS OF MILITARY PSYCHOTRAUMA

Introduction. War is a super-powerful extreme event that causes adaptive capacity to decline. The need for a clearer understanding of the specifics of war psychotrauma and its clinical consequences for all components of human health determines the relevance of the topic of this study.&#x0D; The aim of the work is to study the peculiarities of psychosomatic relationships in persons with the consequences of war psychotrauma.&#x0D; Materials and methods. The experimental study group consisted of 32 patients from the number of temporarily displaced persons, the control group – 34 patients who did not change their place of residence during the entire period of the full-scale war. The diagnosis was aimed at identifying neurotic, stress-related and somatoform disorders, according to the rubric (F40-F48) of the ICD-10. Assessment of the somatic condition was carried out on the basis of analysis of anamnestic data, clinical examination of the patient and indicators of laboratory tests. Psychodiagnostic methods were used to assess the mental states of patients (Clinical Questionnaire for the Detection and Assessment of Neurotic States; Methodology for the express diagnosis of neurosis by K. Heck and H. Hess; Questionnaire SAN (Mood, Activity, Mood); Methodology for the diagnosis of Taylor's anxiety level; Self-assessment of mental states according to Eysenck; Ch. Spibleger's Reactive and Personal Anxiety Assessment Scale in the modification of Y. Hanin. Statistical processing of the research results was carried out in the Microsoft Excel program and with the help of the Social Science Statistics online calculator.&#x0D; Results. As a result of the analysis of anamnestic data, clinical examination and analysis of laboratory tests, psychosomatic disorders were found in patients of both research groups: in 29 (90.63%) patients of the experimental group and in 26 (74.47%) patients of the control group.&#x0D; Conclusions. It has been confirmed that a prolonged stressful extreme situation of a full-scale war causes the formation of negative mental states accompanied by psychosomatic manifestations, the formation of a closed vicious circle of a pathological psychosomatic process. A high close direct correlation was found between the level of reactive anxiety, autonomic disorders, well-being, activity, mood of patients and psychosomatic symptoms. The absence of a statistically significant difference in the risk of developing psychosomatic disorders as a result of war psychotrauma in temporarily displaced persons and those who did not change their place of residence was revealed.

Commentary on "Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning".

Commentary on "Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning".

Predicting Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery: A Step Towards True "Precision" Medicine?: Commentary on "Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning".

Predicting Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery: A Step Towards True "Precision" Medicine?: Commentary on "Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning".

Features of nephrology care in Ukraine during martial law

Background. All the research data available in the world literature on this problem are mostly unpublished and relate to the provision of hemodialysis care in Syria, Afghanistan and Serbia during military conflicts, as well as extreme natural phenomena, for example, in Turkey, Japan and the USA. During this time, technical and information support and methods of treatment with pharmaceuticals have changed significantly. Therefore, new realities and extreme conditions of providing nephrology care in the conditions of martial law require new views and algorithms to preserve the life and health of patients with chronic kidney disease (CKD). The purpose: to present data on clinical observation and treatment of patients with CKD stage 1–5 D/T in Ukraine during martial law. The object of the study is CKD stage 1–5 D/T in patients in Ukraine during martial law. The subject of the research is the organization of providing nephrology care to patients with CKD stage 1–5 D/T in Ukraine during martial law. Materials and methods. The study is retro- and prospective; the primary documentation of patients who applied for nephrology care from February 24, 2022 to October 24, 2023 was examined. To study the obtained results, we used data available in open sources, which were subject to SWOT analysis and, if possible, statistical processing using online calculators. Results. According to the United Nations, the population of Ukraine during the military operations decreased by more than 6 million, of which at least 25 % were children. In addition, internal migration is documented, which objectively also negatively affected the ability to provide qualified nephrology care. Regarding the provision of medical care, nephrology patients, who make up an ave­rage of 10 % of the population, form a fairly significant group. The most available statistics during the martial law in Ukraine are on patients receiving renal replacement therapy. In the first 6 months from the beginning of hostilities, emigration in the form of refugees to the European Union amounted to 602 patients receiving dialysis, more than 400 remained in the territories temporarily not controlled by Ukraine. However, from the entire pre-war registry, we still do not have reliable information about more than 1,000 patients who received renal replacement therapy. During the military operations, we proposed to distinguish 4 zones, which differ in their characteristics in terms of the possibilities of providing nephrology care. Conclusions. With the beginning of martial law in Ukraine, primary difficulties arose in the provision of nephrology care. The results of the study showed that these difficulties are related to the unpreparedness of specialized medical care for current challenges and to solve issues that arise in wartime, and are due to a shortage of personnel, medicines and consumables. However, martial law gave impetus to the development of new solutions that proved to be quite successful in improving the provision of nephrology care.

Evaluation of readability levels of online patient education materials for female pelvic floor disorders

Most women hesitate to seek help from healthcare providers as they find it difficult to share complaints of involuntary leakage or vaginal prolapse. Hence, they often refer to the websites of national and/or international bodies’ patient education materials (PEMs), which are considered the most reliable sources. The crucial factor that determines their usefulness is their readability level, which makes them “easy” or “difficult” to read, and is recommended, not to exceed the sixth grade level. In this study, we aimed to assess the readability levels of Turkish translated PEMs from the websites of the International Urogynecological Association and the European Association of Urology and the PEMs originally written in Turkish from the website of the Society of Urological Surgery in Turkey. All the PEMs (n = 52) were analyzed by online calculators using the Atesman formula, Flesch-Kincaid grade level, and Gunning Fog index. The readability parameters, number of sentences, words, letters, syllables, and readability intervals of these methods were compared among the groups using the Kruskal-Wallis test, or ANOVA test, with post hoc comparisons where appropriate. The readability level of all PEMs is at least at an “averagely difficult” interval, according to both assessment methods. No significant differences were found among the PEM groups in terms of readability parameters and assessment methods (P &gt; .05). Whether original or translated, international or national societies’ PEMs’ readability scores were above the recommended level of sixth grade. Thus, the development of PEMs needs to be revised accordingly by relevant authorities.

Preoperative risk stratification for patients with ≤ 1cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: construction of a dynamic predictive model.

The aim of this study was to investigate the relationships and predictive value of preoperative peripheral blood inflammatory markers as a means by which to assess risk for patients with ≤ 1cm papillary thyroid carcinomas (PTCs). In addition, a preoperative risk stratification predictive model was constructed and validated. Clinical and pathologic data, as well as preoperative blood specimens, were collected from patients who underwent initial thyroid cancer surgery at the Hangzhou First People's Hospital, from January 2014 to January 2023. Risk assessment was performed based on postoperative pathology according to the 2015 ATA guidelines for recurrence risk stratification. Using univariate analysis and multivariate logistic regression, we identified independent risk factors associated with risk stratification. A predictive model was established and its discriminative and calibration abilities were validated. An independent validation dataset was used to verify the model, and the model was deployed as an online calculator. A total of 1326 patients were included in the study, with 1047 cases (79.0%) classified as low risk and 279 cases (21.0%) classified as intermediate to high risk. The modeling group consisted of 981 cases, through univariate analysis and multivariate logistic regression analysis, preoperative blood Neutrophil/Lymphocyte Ratio (NLR), gender, tumor diameter, and multifocality were identified as independent risk factors that distinguished between low and intermediate to high risk patients with ≤ 1cm PTCs. The clinical predictive model exhibited an AUC of 0.785, specificity of 70.6%, and sensitivity of 75.8%. For the independent validation group of 345 patients, the AUC was 0.813, specificity was 83.8%, and sensitivity was 70.4%. The calibration curve and clinical decision curve indicate that the model demonstrates excellent calibration performance. A dynamic clinical predictive model based on preoperative blood NLR and clinical information for patients with ≤ 1cm PTCs was established. The model is useful for preoperative risk assessment of patients with ≤ 1cm PTCs.

Volta: A Tool for Battery Screening Bridging the Gap between Virtual Electrode Materials and Practical Applications

For millennia, materials science has relied on the paradigm of empiricism to describe the physical world and to fuel technological advance. It was only some centuries ago that the paradigm of mathematical modelling was introduced, boosting scientific knowledge with the introduction of laws in the form of equations. Their predictive capability proved essential to the human understanding of nature, but, as the complexity of the problems increased over time, equations became more and more impenetrable, with analytical solutions being often rather challenging to determine. Electronic computers cut this Gordian knot for us, allowing for quick(er) calculations of approximate solutions to the equations mentioned above. [1]Battery science has greatly benefit from these advances: Density Functional Theory allowed for e.g. reliable quantifications of active materials’ figures of merit like stability or operational potential. The introduction of multiscale modelling was also allowed by this third paradigm of science, leveraging quick computation speeds to infer cell level properties from lower-level ones. [2]The data generated both with experimentation and computation has now accumulated, and a great amount is being published on platforms like, but not limited to, the Materials Project. [3] This is where science enters the so-called fourth paradigm, i.e. data driven science, [4] where statistical methods can contaminate and enhance the “traditional” physico-chemical ones.In fact, new approaches to materials screening are being published with increasing frequency, like neural networks able to predict material properties [5], or generative algorithms able to suggest novel materials. [6] It is important to notice that these methods rely heavily on data quality, hence are not to be seen as a substitute for first principle calculations, but rather as techniques able to expand their scope and capabilities.Battery science itself can greatly benefit from the fourth paradigm of science as well: it is in this context that we developed the present work, in an endeavor to tackle a specific issue in battery science: active materials screening has rarely considered the properties that emerge when coupling two active materials, embedding them into composite electrodes and connecting them electrically.The present work introduces a Python-based combinatorial materials screening workflow, named VOLTA. Its pipeline allows for a novel battery active material explorative workflow, that prioritizes the cell level performance indicators, like cell capacity and voltage profile. This is achieved by the construction of a starting dataset of both observed and virtual active materials from the Materials Project, the implementation of a physics-based pipeline for the assessment of practical electrode properties like porosity and thickness, [7] and the coupling of the electrodes into virtual cells, whose figures of merit are calculated, like voltage and capacity. The screening can be conducted by applying filters to these cell-level properties, achieving an indirect selection of the most suitable active materials.The approach is validated through comparison to current commercial battery technology, and we demonstrate that VOLTA is able to identify promising electrode materials for high energy batteries, like the well-known LCO, LNO and graphite. We also illustrate a case-study, where the pipeline is used in a low-voltage "battery discovery" problem. Bibliography [1] Agrawal, A., & Choudhary, A. (2016). Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Materials, 4(5), 053208.[2] Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002.[3] Franco, A. A., Rucci, A., Brandell, D., Frayret, C., Gaberscek, M., Jankowski, P., & Johansson, P. (2019). Boosting Rechargeable Batteries R&D by Multiscale Modeling: Myth or Reality? Chemical Reviews, 119(7), 4569–4627.[4] Hey, T., Tansley, S., Tolle, K., & Gray, J. (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. The Fourth Paradigm: Data-Intensive Scientific Discovery, 39–44.[5] Wang, A. Y.-T., Kauwe, S. K., Murdock, R. J., & Sparks, D. (2021). Compositionally-Restricted Attention-Based Network for Materials Property Predictions. Npj Computational Materials, 33.[6] Dan, Y., Zhao, Y., Li, X., Li, S., Hu, M., & Hu, J. (2020). Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. Npj Computational Materials, 6(1), 84.[7] Lombardo, T., Caro, F., Ngandjong, A. C., Hoock, J. B., Duquesnoy, M., Delepine, J. C., Ponchelet, A., Doison, S., & Franco, A. A. (2022). The ARTISTIC Online Calculator: Exploring the Impact of Lithium-Ion Battery Electrode Manufacturing Parameters Interactively Through Your Browser. Batteries & Supercaps, 5(3), e202100324.

Myopia progression patterns among paediatric patients in a clinical setting.

This retrospective analysis of electronic medical record (EMR) data investigated the natural history of myopic progression in children from optometric practices in Ireland. The analysis was of myopic patients aged 7-17 with multiple visits and not prescribed myopia control treatment. Sex- and age-specific population centiles for annual myopic progression were derived by fitting a weighted cubic spline to empirical quantiles. These were compared to progression rates derived from control group data obtained from 17 randomised clinical trials (RCTs) for myopia. Linear mixed models (LMMs) were used to allow comparison of myopia progression rates against outputs from a predictive online calculator. Survival analysis was performed to determine the intervals at which a significant level of myopic progression was predicted to occur. Myopia progression was highest in children aged 7 years (median: -0.67 D/year) and progressively slowed with increasing age (median: -0.18 D/year at age 17). Female sex (p < 0.001), a more myopic SER at baseline (p < 0.001) and younger age (p < 0.001) were all found to be predictive of faster myopic progression. Every RCT exhibited a mean progression higher than the median centile observed in the EMR data, while clinic-based studies more closely matched the median progression rates. The LMM predicted faster myopia progression for patients with higher baseline myopia levels, in keeping with previous studies, which was in contrast to an online calculator that predicted slower myopia progression for patients with higher baseline myopia. Survival analysis indicated that at a recall period of 12 months, myopia will have progressed in between 10% and 70% of children, depending upon age. This study produced progression centiles of untreated myopic children, helping to define the natural history of untreated myopia. This will enable clinicians to better predict both refractive outcomes without treatment and monitor treatment efficacy, particularly in the absence of axial length data.

Cure Probabilities After Resection of Pancreatic Ductal Adenocarcinoma: A Multi-Institutional Analysis of 2554 Patients.

To assess the probability of being cured of pancreatic ductal adenocarcinoma (PDAC) by pancreatic surgery. Statistical cure implies that a patient treated for a specific disease will have the same life expectancy as if he/she never had that disease. Patients who underwent pancreatic resection for PDAC between 2010 and 2021 were retrospectively identified using a multi-institutional database. A nonmixture statistical cure model was applied to compare disease-free survival to the survival expected for a matched general population. Among 2554 patients, either in the setting of upfront (n=1691) or neoadjuvant strategy (n=863), the cure model showed that the probability that surgery would offer the same life expectancy (and tumor-free) as the matched general population was 20.4% (95% CI: 18.3, 22.5). Cure likelihood reached the 95% of certainty (time to cure) after 5.3 years (95% CI: 4.7, 6.0). A preoperative model was developed based on tumor stage at diagnosis ( P =0.001), radiologic size ( P =0.001), response to chemotherapy ( P =0.007), American Society of Anesthesiology class ( P =0.001), and preoperative Ca19-9 ( P =0.001). A postoperative model with the addition of surgery type ( P =0.015), pathologic size ( P =0.001), tumor grading ( P =0.001), resection margin ( P =0.001), positive lymph node ratio ( P =0.001), and the receipt of adjuvant therapy ( P =0.001) was also developed. Patients operated for PDAC can achieve a life expectancy similar to that of the general population, and the likelihood of cure increases with the passage of recurrence-free time. An online calculator was developed and available at https://aicep.website/?cff-form=15 .

Prospective evaluation of the ESCRS online calculator for calculation of a multifocal intraocular lens.

To evaluate a recently introduced ESCRS online calculator for intraocular lens (IOL) calculation of a multifocal IOL in refractive lens exchange and cataract surgery in a prospective setting. Department of Ophthalmology, Goethe University Frankfurt, Germany. Prospective, consecutive case series. We included eyes that received lens extraction and multifocal IOL implantation. We compared the mean prediction error (MPE), mean and median absolute prediction error (MAE, MedAE) of the online calculator provided by the European Society of Cataract and Refractive Surgeons (ESCRS), and number of eyes within ±0.5, ±1.0, ±2.0 diopters (D) of target refraction. Additionally, the SRK/T formula was included for comparison as well. Postoperative spherical equivalent was measured at 3 months. Only one eye per patient was included. Eighty-eight eyes from 88 patients with mean age of 62 years ± 9.5 were included. The MedAE was low for all formulas and ranged from 0.26D (Kane), Hill-RBF (0.27D), Hoffer QST (0.27D), Barrett (0.28D), EVO (0.29D), Cooke K6 (0.27D), 0.30D (Pearl DGS) to 0.31D (SRK/T). No statistically significant difference was found (p = 0.627). Considering the number of eyes within ±0.5D of the calculated refraction the best performing was again the Hill-RBF (84%, 74 eyes), again followed by Kane (71, 81%), EVO, Pearl DGS, Hoffer QST, Barrett (each 80%, 70 eyes),Cooke K6 (78%, 69 eyes), and SRK/T (74%). Again, no statistically significant difference was found (p = 0.39). Using recently introduced ESCRS online IOL calculator in multifocal intraocular lenses leads to a high number of eyes reaching target refraction and low prediction errors. All formulas performed similar well. However, Hill-RBF showed the highest number of eyes within ±0.5D, but no significance was found regarding this.