124,674 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Red Cell Distribution Width is independently associated with Mortality in Sepsis
No full textBackground: Mortality in sepsis remains high. Studies in small cohorts have shown that red cell distribution width (RDW) is associated with mortality. The aim of this study was to validate these findings in a large multi-centre cohort. Methods: We conducted this retrospective analysis of the multi-center eICU Collaborative Research Database in 16,423 septic patients. We split the cohort in patients with low (≤15%; n=7,129) and high (>15%; n=9,294) RDW. Univariable and multivariable multilevel logistic regression were used to fit regression models for the binary primary outcome of hospital mortality and the secondary outcome ICU mortality with hospital unit as random effect. Optimal cut-offs were calculated using the Youden-index. Results: Patients with high RDW were more often older than 65 years (57% vs. 50%; p<0.001) and had higher APACHE IV scores (69 vs. 60 pts.; p<0.001). Both hospital- (aOR 1.18 95%CI 1.16-1.20; p<0.001) and ICU-mortality (aOR 1.16 95%CI 1.14-1.18; p<0.001) were associated with RDW as a continuous variable. Patients with high RDW had a higher hospital mortality (20 vs. 9%; aOR 2.63 95%CI 2.38-2.90; p<0.001). This finding persisted after multivariable adjustment (aOR 2.14 95%CI 1.93-2.37; p<0.001) in a multilevel logistic regression analysis. The optimal RDW-cut-off for prediction of hospital mortality was 16%. Conclusion: We found an association of RDW with mortality in septic patients and propose an optimal cut-off value for risk stratification. In a combined model with lactate, RDW shows equivalent diagnostic performance to SOFA score and APACHE IV
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Gastroenterologist against the machine - opportunities and limitations of machine learning models for prediction of advanced adenoma
No full textBackground & Aims Screening for colorectal cancer (CRC) relies on colonoscopy and/or fecal occult blood test while other (non-invasive) risk-stratification systems have not been implemented into European guidelines. Here, we evaluated the potential of Machine Learning (ML) methods to optimize prediction of advanced adenoma (AA).
Patients & Methods 5862 individuals participating in a screening program for colorectal cancer were included after excluding patients with history of CRC, symptomatic patients and those with insufficient colonoscopy. Adenoma were diagnosed histologically with AA being ≥1cm in size, or high-grade dysplasia/ villous features being present. Clinical, laboratory and lifestyle parameters were assessed at the time of colonoscopy. Logistic regression (LR) and extreme gradient boosting algorithms (XGBoost) were evaluated for AA-prediction based on readily-available laboratory/clinical/lifestyle parameters. The dataset was divided into a derivation cohort (for model development and internal cross-validation) and an external validation cohort.
Results The mean age was 58.7±9.7 years with 2811 males (48.0 %). 1404 (24.0 %) suffered from obesity (BMI≥30kg/m2), 871 (14.9 %) from diabetes, and 2095 (39.1 %) from the metabolic syndrome. Any adenoma was detected in 1884 (32.1 %) and any AA in 437 (7.5 %). 659 individuals (11.2 %) had a first-degree relative with a history of CRC. Modelling 36 laboratory parameters, 8 clinical parameters and data on 8 food types/dietary patterns, a moderate accuracy to predict AA with XGBoost (AUC of 0.66-0.68) and LR (AUC of 0.65-0.66) could be achieved. Limiting variables to established risk factors for AA did not significantly improve performance. Also, subgroup analyses in subjects without genetic predisposition or gender-specific analyses showed similar results.
Conclusion ML, based on point prevalence laboratory and clinical information, does not accurately predict AA. Non-invasive risk-prediction seems insufficient to replace current CRC screening programs. However, the potential for sequential application before colonoscopy to increase pre-test probability warrants further investigation
Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma
No full textScreening for colorectal cancer (CRC) continues to rely on colonoscopy and/or fecal occult blood testing since other (non-invasive) risk-stratification systems have not yet been implemented into European guidelines. In this study, we evaluate the potential of machine learning (ML) methods to predict advanced adenomas (AAs) in 5862 individuals participating in a screening program for colorectal cancer. Adenomas were diagnosed histologically with an AA being ≥ 1 cm in size or with high-grade dysplasia/villous features being present. Logistic regression (LR) and extreme gradient boosting (XGBoost) algorithms were evaluated for AA prediction. The mean age was 58.7 ± 9.7 years with 2811 males (48.0%), 1404 (24.0%) of whom suffered from obesity (BMI ≥ 30 kg/m2), 871 (14.9%) from diabetes, and 2095 (39.1%) from metabolic syndrome. An adenoma was detected in 1884 (32.1%), as well as AAs in 437 (7.5%). Modelling 36 laboratory parameters, eight clinical parameters, and data on eight food types/dietary patterns, moderate accuracy in predicting AAs with XGBoost and LR (AUC-ROC of 0.65–0.68) could be achieved. Limiting variables to established risk factors for AAs did not significantly improve performance. Moreover, subgroup analyses in subjects without genetic predispositions, in individuals aged 45–80 years, or in gender-specific analyses showed similar results. In conclusion, ML based on point-prevalence laboratory and clinical information does not accurately predict AAs
The impact of ethnic background on ICU care and outcome in sepsis and septic shock – a retrospective multicenter analysis on 17,949 patients
No full textBackground
Previous studies have been inconclusive about racial disparities in sepsis. This study evaluated the impact of ethnic background on management and outcome in sepsis and septic shock.
Methods
This analysis included 17,146 patients suffering from sepsis and septic shock from the multicenter eICU Collaborative Research Database. Generalized estimated equation (GEE) population-averaged models were used to fit three sequential regression models for the binary primary outcome of hospital mortality.
Results
Non-Hispanic whites were the predominant group (n = 14,124), followed by African Americans (n = 1,852), Hispanics (n = 717), Asian Americans (n = 280), Native Americans (n = 146) and others (n = 830). Overall, the intensive care treatment and hospital mortality were similar between all ethnic groups. This finding was concordant in patients with septic shock and persisted after adjusting for patient-level variables (age, sex, mechanical ventilation, vasopressor use and comorbidities) and hospital variables (teaching hospital status, number of beds in the hospital).
Conclusion
We could not detect ethnic disparities in the management and outcomes of critically ill septic patients and patients suffering from septic shock. Disparate outcomes among critically ill septic patients of different ethnicities are a public health, rather than a critical care challenge
Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation
No full textPurpose: To evaluate the application of machine learning methods, specifically Deep Neural Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict mortality within 96 hours after admission to mirror the clinical situation of patient evaluation after an ICU trial, which consists of 24-48 hours of ICU treatment and then “re-triage”. The input variables were deliberately restricted to ABG values to maximise real-world practicability.
Methods: We retrospectively evaluated septic patients in the multi-centre eICU dataset as well as single centre MIMIC-III dataset. Included were all patients alive after 48 hours with available data on ABG (n = 3979 and n = 9655 ICU stays for the multi-centre and single centre respectively). The primary endpoint was 96 -h-mortality.
Results: The model was developed using long short-term memory (LSTM), a type of DNN designed to learn temporal dependencies between variables. Input variables were all ABG values within the first 48 hours. The SOFA score (AUC of 0.72) was moderately predictive. Logistic regression showed good performance (AUC of 0.82). The best performance was achieved by the LSTM-based model with AUC of 0.88 in the multi-centre study and AUC of 0.85 in the single centre study.
Conclusions: An LSTM-based model could help physicians with the “re-triage” and the decision to restrict treatment in patients with a poor prognosis
Pragmatic Case Studies as a Source of Unity in Applied Psychology
No full textTo unify or not to unify applied psychology: that is the question. In this article we review pendulum swings in the historical efforts to answer this question—from a comprehensive, positivist, “top-down,” deductive yes between the 1930s and the early 60s, to a postmodern no since then. A rationale and proposal for a limited, “bottom-up,” inductive yes in applied psychology is then presented, employing a case-based paradigm that integrates both positivist and postmodern themes and components. This paradigm is labeled “pragmatic psychology” and, its specific use of case studies, the “Pragmatic Case Study Method” (“PCS Method”). We call for the creation of peer-reviewed journal-databases of pragmatic case studies as a foundational source of unifying applied knowledge in our discipline. As one example, the potential of the PCS Method for unifying different angles of theoretical regard is illustrated in an area of applied psychology, psychotherapy, via the case of Mrs. B. The article then turns to the broader historical and epistemological arguments for the unifying nature of the PCS Method in both applied and basic psychology.Peer reviewe
Dr. Edwin Wright Collection: Author Unknown
No full textNotes - The author relates several short stories about his neighbours including Alex McDonell, homesteading and life around Meanook and Athabasca (1 page
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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