nils-herrmann/clean_scirepeval huggingface dataset:
The care transitions intervention: results of a randomized controlled trial.

Patients with complex care needs who require care across different health care settings are vulnerable to experiencing serious quality problems. A care transitions intervention designed to encourage patients and their caregivers to assert a more active role during care transitions may reduce rehospitalization rates.



Randomized controlled trial. Between September 1, 2002, and August 31, 2003, patients were identified at the time of hospitalization and were randomized to receive the intervention or usual care. The setting was a large integrated delivery system located in Colorado. Subjects (N = 750) included community-dwelling adults 65 years or older admitted to the study hospital with 1 of 11 selected conditions. Intervention patients received (1) tools to promote cross-site communication, (2) encouragement to take a more active role in their care and to assert their preferences, and (3) continuity across settings and guidance from a "transition coach." Rates of rehospitalization were measured at 30, 90, and 180 days.



Intervention patients had lower rehospitalization rates at 30 days (8.3 vs 11.9, P = .048) and at 90 days (16.7 vs 22.5, P = .04) than control subjects. Intervention patients had lower rehospitalization rates for the same condition that precipitated the index hospitalization at 90 days (5.3 vs 9.8, P = .04) and at 180 days (8.6 vs 13.9, P = .046) than controls. The mean hospital costs were lower for intervention patients ($2058) vs controls ($2546) at 180 days (log-transformed P = .049).



Coaching chronically ill older patients and their caregivers to ensure that their needs are met during care transitions may reduce the rates of subsequent rehospitalization.
PageRanking WordNet Synsets: An Application to Opinion Mining

This paper presents an application of PageRank, a random-walk model originally devised for ranking Web search results, to ranking WordNet synsets in terms of how strongly they possess a given semantic property. The semantic properties we use for exemplifying the approach are positivity and negativity, two properties of central importance in sentiment analysis. The idea derives from the observation that WordNet may be seen as a graph in which synsets are connected through the binary relation “a term belonging to synset sk occurs in the gloss of synset si”, and on the hypothesis that this relation may be viewed as a transmitter of such semantic properties. The data for this relation can be obtained from eXtended WordNet, a publicly available sensedisambiguated version of WordNet. We argue that this relation is structurally akin to the relation between hyperlinked Web pages, and thus lends itself to PageRank analysis. We report experimental results supporting our intuitions.
Learning hierarchical category influence on both users and items for effective recommendation

Item category has proven to be useful additional information to address the data sparsity and cold start problems in recommender systems. Although categories have been well studied in which they are independent and structured in a flat form, in many real applications, item category is often organized in a richer knowledge structure - category hierarchy, to reflect the inherent correlations among different categories. In this paper, we propose a novel latent factor model by exploiting category hierarchy from the perspectives of both users and items for effective recommendation. Specifically, a user can be influenced by her preferred categories in the hierarchy. Similarly, an item can be characterized by the associated categories in the hierarchy. We incorporate the influence that different categories have towards a user and an item in the hierarchical structure. Experimental results on two real-world data sets demonstrate that our method consistently outperforms the state-of-the-art category-aware recommendation algorithms.
TOWARD OPTIMAL FEATURE SELECTION USING RANKING METHODS AND CLASSIFICATION ALGORITHMS

We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.
Optimal Symbiosis and Fair Scheduling in Shared Cache

On multi-core processors, applications are run sharing the cache. This paper presents optimization theory to co-locate applications to minimize cache interference and maximize performance. The theory precisely specifies MRC-based composition, optimization, and correctness conditions. The paper also presents a new technique called footprint symbiosis to obtain the best shared cache performance under fair CPU allocation as well as a new sampling technique which reduces the cost of locality analysis. When sampling and optimization are combined, the paper shows that it takes less than 0.1 second analysis per program to obtain a co-run that is within 1.5 percent of the best possible performance. In an exhaustive evaluation with 12,870 tests, the best prior work improves co-run performance by 56 percent on average. The new optimization improves it by another 29 percent. Without single co-run test, footprint symbiosis is able to choose co-run choices that are just 8 percent slower than the best co-run solutions found with exhaustive testing.
Elastic stack in action for smart cities: Making sense of big data

Having good decision support is absolutely necessary nowadays because of the need to improve and gain value. For any organization, it is vital to obtain value from anything it can and having huge amounts of data, Big Data,_pushes them to do so. But only having Big Data is not enough. The most important thing is to use it smartly in order to gain valuable decision support. Making sense of Big Data would happen when we are able to use all the data we have and get important hints and directions. The impediment in the Big Data concept is to obtain support as fast it can be obtained, in real time if possible and the solution used needs to be very malleable because of information technology evolution. Because of this evolution and because of the ease in managing data with Elastic Stack we chose to use Elastic Stack to manage Big Data. The process of making sense of Big Data is based on three big steps: collect, process and use. In order to do so and to make sense of all of this, this paper proposes to use an Elastic Stack solution, also known as ELK (Elasticsearch, Logstash and Kibana), to easily and rapidly manage the Big Data problem. The purpose of making sense of Big Data is succeeding to extract valuable information to stand as decision support. In order to achieve that purpose and obtain valuable information from Big Data all components are used to process data and analyze the result to offer support for decision making.
GTrans: Generic Knowledge Graph Embedding via Multi-State Entities and Dynamic Relation Spaces

Knowledge graph embedding aims to construct a low-dimensional and continuous space, which is able to describe the semantics of high-dimensional and sparse knowledge graphs. Among existing solutions, translation models have drawn much attention lately, which use a relation vector to translate the head entity vector, the result of which is close to the tail entity vector. Compared with classical embedding methods, translation models achieve the state-of-the-art performance; nonetheless, the rationale and mechanism behind them still aspire after understanding and investigation. In this connection, we quest into the essence of translation models, and present a generic model, namely, GTrans, to entail all the existing translation models. In GTrans, each entity is interpreted by a combination of two states—eigenstate and mimesis. Eigenstate represents the features that an entity intrinsically owns, and mimesis expresses the features that are affected by associated relations. The weighting of the two states can be tuned, and hence, dynamic and static weighting strategies are put forward to best describe entities in the problem domain. Besides, GTrans incorporates a dynamic relation space for each relation, which not only enables the flexibility of our model but also reduces the noise from other relation spaces. In experiments, we evaluate our proposed model with two benchmark tasks—triplets classification and link prediction. Experiment results witness significant and consistent performance gain that is offered by GTrans over existing alternatives.
Information Assurance in B2C e-Commerce Transactions: A Process-Oriented Framework and Empirical Analysis of Websites using Semantic Networks

What aspects of information assurance can be identified in Business-to-Consumer (B-toC) online transactions? The purpose of this research is to build a theoretical framework for studying information assurance based on a detailed analysis of academic literature for online exchanges in B-to-C electronic commerce. Further, a semantic network content analysis is conducted to analyze the representations of information assurance in B-to-C electronic commerce in the real online market place (transaction Web sites of selected Fortune 500 firms). The results show that the transaction websites focus on some perspectives and not on others. For example, we see an emphasis on the importance of technological and consumer behavioral elements of information assurance such as issues of online security and privacy. Further corporate practitioners place most emphasis on transaction-related information assurance issues. Interestingly, the product and institutional dimension of information assurance in online transaction websites are only
Defining immunological dysfunction in sepsis: A requisite tool for precision medicine.

Immunological dysregulation is now recognised as a major pathogenic event in sepsis. Stimulation of immune response and immuno-modulation are emerging approaches for the treatment of this disease. Defining the underlying immunological alterations in sepsis is important for the design of future therapies with immuno-modulatory drugs.



Clinical studies evaluating the immunological response in adult patients with Sepsis and published in PubMed were reviewed to identify features of immunological dysfunction. For this study we used key words related with innate and adaptive immunity.



Ten major features of immunological dysfunction (FID) were identified involving quantitative and qualitative alterations of [antigen presentation](FID1), [T and B lymphocytes] (FID2), [natural killer cells] (FID3), [relative increase in T regulatory cells] (FID4), [increased expression of PD-1 and PD-ligand1](FID5), [low levels of immunoglobulins](FID6), [low circulating counts of neutrophils and/or increased immature forms in non survivors](FID7), [hyper-cytokinemia] (FID8), [complement consumption] (FID9), [defective bacterial killing by neutrophil extracellular traps](FID10).



This review article identified ten major features associated with immunosuppression and immunological dysregulation in sepsis. Assessment of these features could help in utilizing precision medicine for the treatment of sepsis with immuno-modulatory drugs.
Statistical power analysis for growth curve models using SAS.

Power analysis is critical in research designs. This study discusses a simulation-based approach utilizing the likelihood ratio test to estimate the power of growth curve analysis. The power estimation is implemented through a set of SAS macros. The application of the SAS macros is demonstrated through several examples, including missing data and nonlinear growth trajectory situations. The results of the examples indicate that the power of growth curve analysis increases with the increase of sample sizes, effect sizes, and numbers of measurement occasions. In addition, missing data can reduce power. The SAS macros can be modified to accommodate more complex power analysis for both linear and nonlinear growth curve models.
The Friedman-Eilber Resection Arthroplasty of the Pelvis

It has been argued that internal hemipelvectomy without reconstruction of the pelvic ring leads to poor ambulation and inferior patient acceptance. To determine the accuracy of this contention, we posed the following questions: First, how effectively does a typical patient ambulate following this procedure? Second, what is the typical functional capacity of a patient following internal hemipelvectomy? In the spring of 2006, we obtained video documentation of eight patients who had undergone resection arthroplasty of the hemipelvis seen in our clinic during routine clinical followup. The minimum followup in 2006 was 1.1 years (mean, 8.2 years; range, 1.1-22.7 years); at the time of last followup in 2008 the minimum followup was 2.9 years (mean, 9.8 years; range, 2.9-24.5 years). At last followup seven of the eight patients were without pain, and were able to walk without supports. The remaining patient used narcotic medication and a cane or crutch only occasionally. The mean MSTS score at the time of most recent followup was 73.3% of normal (range 53.3-80.0%; mean raw score was 22.0; range 16-24). All eight patients ultimately returned to gainful employment. These observations demonstrate independent painless ambulation and acceptable function is possible following resection arthroplasty of the hemipelvis.


 See Guidelines for Authors for a complete description of levels of evidence.
Four-year follow-up of TYPHOON (trial to assess the use of the CYPHer sirolimus-eluting coronary stent in acute myocardial infarction treated with BallOON angioplasty).

The aim of this study was to assess the long-term safety and efficacy of the CYPHER (Cordis, Johnson and Johnson, Bridgewater, New Jersey) sirolimus-eluting coronary stent (SES) in percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI).



Concern over the safety of drug-eluting stents implanted during PCI for STEMI remains, and long-term follow-up from randomized trials are necessary. TYPHOON (Trial to assess the use of the cYPHer sirolimus-eluting stent in acute myocardial infarction treated with ballOON angioplasty) randomized 712 patients with STEMI treated by primary PCI to receive either SES (n = 355) or bare-metal stents (BMS) (n = 357). The primary end point, target vessel failure at 1 year, was significantly lower in the SES group than in the BMS group (7.3% vs. 14.3%, p = 0.004) with no increase in adverse events.



A 4-year follow-up was performed. Complete data were available in 501 patients (70%), and the survival status is known in 580 patients (81%).



Freedom from target lesion revascularization (TLR) at 4 years was significantly better in the SES group (92.4% vs. 85.1%; p = 0.002); there were no significant differences in freedom from cardiac death (97.6% and 95.9%; p = 0.37) or freedom from repeat myocardial infarction (94.8% and 95.6%; p = 0.85) between the SES and BMS groups. No difference in definite/probable stent thrombosis was noted at 4 years (SES: 4.4%, BMS: 4.8%, p = 0.83). In the 580 patients with known survival status at 4 years, the all-cause death rate was 5.8% in the SES and 7.0% in the BMS group (p = 0.61).



In the 70% of patients with complete follow-up at 4 years, SES demonstrated sustained efficacy to reduce TLR with no difference in death, repeat myocardial infarction or stent thrombosis.
Prevention of cardiovascular disease in rheumatoid arthritis.

The increased risk of cardiovascular disease (CVD) in rheumatoid arthritis (RA) has been recognized for many years. However, although the characteristics of CVD and its burden resemble those in diabetes, the focus on cardiovascular (CV) prevention in RA has lagged behind, both in the clinical and research settings. Similar to diabetes, the clinical picture of CVD in RA may be atypical, even asymptomatic. Therefore, a proactive screening for subclinical CVD in RA is warranted. Because of the lack of clinical trials, the ideal CVD prevention (CVP) in RA has not yet been defined. In this article, we focus on challenges and controversies in the CVP in RA (such as thresholds for statin therapy), and propose recommendations based on the current evidence. Due to the significant contribution of non-traditional, RA-related CV risk factors, the CV risk calculators developed for the general population underestimate the true risk in RA. Thus, there is an enormous need to develop adequate CV risk stratification tools and to identify the optimal CVP strategies in RA. While awaiting results from randomized controlled trials in RA, clinicians are largely dependent on the use of common sense, and extrapolation of data from studies on other patient populations. The CVP in RA should be based on an individualized evaluation of a broad spectrum of risk factors, and include: 1) reduction of inflammation, preferably with drugs decreasing CV risk, 2) management of factors associated with increased CV risk (e.g., smoking, hypertension, hyperglycemia, dyslipidemia, kidney disease, depression, periodontitis, hypothyroidism, vitamin D deficiency and sleep apnea), and promotion of healthy life style (smoking cessation, healthy diet, adjusted physical activity, stress management, weight control), 3) aspirin and influenza and pneumococcus vaccines according to current guidelines, and 4) limiting use of drugs that increase CV risk. Rheumatologists should take responsibility for the education of health care providers and RA patients regarding CVP in RA. It is immensely important to incorporate CV outcomes in testing of anti-rheumatic drugs.
Long-Term Complications of Polyethylene Glycol Injection to the Face

Currently, filling, smoothing, or recontouring the face through the use of injectable fillers is one of the most popular forms of cosmetic surgery. Because these materials promise a more youthful appearance without anesthesia in a noninvasive way, various fillers have been used widely in different parts of the world. However, most of these fillers have not been approved by the Food and Drug Administration, and their applications might cause unpleasant disfiguring complications. This report describes a case of foreign body granuloma in the cheeks secondary to polyethylene glycol injection and shows the possible complications associated with the use of facial fillers.
Effects of carnitine on thyroid hormone action.

By experiments on cells (neurons, hepatocytes, and fibroblasts) that are targets for thyroid hormones and a randomized clinical trial on iatrogenic hyperthyroidism, we validated the concept that L-carnitine is a peripheral antagonist of thyroid hormone action. In particular, L-carnitine inhibits both triiodothyronine (T3) and thyroxine (T4) entry into the cell nuclei. This is relevant because thyroid hormone action is mainly mediated by specific nuclear receptors. In the randomized trial, we showed that 2 and 4 grams per day of oral L-carnitine are capable of reversing hyperthyroid symptoms (and biochemical changes in the hyperthyroid direction) as well as preventing (or minimizing) the appearance of hyperthyroid symptoms (or biochemical changes in the hyperthyroid direction). It is noteworthy that some biochemical parameters (thyrotropin and urine hydroxyproline) were refractory to the L-carnitine inhibition of thyroid hormone action, while osteocalcin changed in the hyperthyroid direction, but with a beneficial end result on bone. A very recent clinical observation proved the usefulness of L-carnitine in the most serious form of hyperthyroidism: thyroid storm. Since hyperthyroidism impoverishes the tissue deposits of carnitine, there is a rationale for using L-carnitine at least in certain clinical settings.
Via-free microstrip to slotline baluns using slotted microstrip cross-junction

This paper presents two novel microstrip-to-slotline baluns. Their design is based on the slotted microstrip cross-junction and its multi-mode equivalent circuit model, i.e., each slotted microstrip supports two modes that have even and odd symmetry. The first balun is a modified version of the conventional 90° via-less microstrip to slotline one with different microstrip and slotline impedances. The 3 dB bandwidth is 2.44 GHz and the minimum insertion loss is 0.5 dB at 2.4 GHz. The second balun is a via-less straight microstrip-to-slotline one that has 3 dB bandwidth of 2.29 GHz and minimum insertion loss of 0.46 dB at 2.4 GHz. Theoretical predictions have been confirmed by EM simulations and measurements.
BIM-Based Parametric Modeling : A Case Study

Parametricism has come to scene as an important style in both architectural design and construction where conventional Computer-Aided Design (CAD) tool has become substandard. Building Information Modeling (BIM) is a recent object-based parametric modeling tool for exploring the relationship between the geometric and non-geometric components of the model. The aim of this research is to explore the capabilities of BIM in achieving variety and flexibility in design extending from architectural to urban scale. This study proposes a method by using User Interface (UI) and Application Programming Interface (API) tools of BIM to generate a complex roof structure as a parametric family. This project demonstrates a dynamic variety in architectural scale. We hypothesized that if a function calculating the roof length is defined using a variety of inputs, it can later be applied to urban scale by utilizing a database of the inputs.
Nutritional and medicinal importance of mushrooms

Mushrooms had long been used for medicinal and food purposes since decades. It is now increasingly recognized that correct diet, controls and modulates many functions of human body and consequently participates in the maintenance of state of good health, necessary to reduce the risk of many diseases. Modern pharmacological research confirms large parts of traditional knowledge regarding the medicinal effects of mushrooms due to their antifungal, antibacterial, antioxidant and antiviral properties, besides being used as functional foods. This paper sums up diverse beneficial health effects of mushrooms to humans, in the form of proteins, carbohydrates, fats, vitamins, minerals, food and drugs, and medicines.
SALAD: Achieving Symmetric Access Latency with Asymmetric DRAM Architecture

Memory access latency has significant impact on application performance. Unfortunately, the random access latency of DRAM has been scaling relatively slowly, and often directly affects the critical path of execution, especially for applications with insufficient locality or memory-level parallelism. The existing low-latency DRAM organizations either incur significant area overhead or burden the software stack with non-uniform access latency. This paper proposes SALAD, a new DRAM device architecture that provides symmetric access l atency with asymmetric DRAM bank organizations. Since local banks have lower data transfer time due to their proximity to the I/O pads, SALAD applies high aspect-ratio (i.e., low-latency) mats only to remote banks to offset the difference in data transfer time, thus providing uniformly low access time (tAC) over the whole device. Our evaluation demonstrates that SALAD improves the IPC by 13 percent (10 percent) without any software modifications, while incurring only 6 percent (3 percent) area overhead.