1,721,204 research outputs found
The Association Between Admission Magnesium Concentrations and Lactic Acidosis in Critical Illness
Full text linkIntroduction: Although magnesium plays an important role in aerobic metabolism and magnesium deficiency is a common phenomenon in critical illness, the association between magnesium deficiency and lactic acidosis in the intensive care unit (ICU) has not been defined. Methods: This was a retrospective, cross-sectional study conducted at a 77 ICU bed tertiary medical center. Data pertaining to the first unique admission of any ICU patient between 2001 and 2008 were extracted from the Multiparameter Intelligent Monitoring in Intensive Care database. Hypomagnesemia was defined as serum magnesium 2 and > 4 mmol/L, respectively. Multivariate modeling was used to explore the association between magnesium and lactate concentrations. Results: Of 8922 critically ill patients, 22.6% were hypomagnesemic. Hypomagnesemia was associated with an increased adjusted risk of mild lactic acidosis (odds ratio [OR] 1.71, 95% confidence interval [95%CI] 1.51-1.94, P < .001) and severe lactic acidosis (OR 1.56, 95%CI 1.32-1.84, P < .001) than the reference quartile. The association between hypomagnesemia and mild lactic acidosis was stronger in those at risk of magnesium deficiency, including diabetics (OR 2.02, 95%CI 1.51-2.72, P < .001) and alcoholics (OR 1.92, 95%CI 1.16-3.19, P =.01). As an internal model control, hypokalemia was not associated with an increased risk of lactic acidosis. Conclusions: Magnesium deficiency is a common finding in patients admitted to the ICU and is associated with lactic acidosis. Our findings support the biologic role of magnesium in metabolism and raise the possibility that hypomagnesemia is a correctable risk factor for lactic acidosis in critical illness
Vital signs as a source of racial bias
No full textBackground racial bias has been shown to be present in clinical data, affecting patients unfairly based on their race, ethnicity and socio-economic status. This problem has the potential to be significantly exacerbated in the light of Artificial Intelligence-aided clinical decision making. We sought to investigate whether bias can be introduced from sources that are considered neutral with respect to ethnicity and race and consequently routinely used in modelling, specifically vital signs.
Methods to perform our analysis, we extracted vital signs from 49,610 admissions from a cohort of adult patients during the first 24 hours after the admission to the Intensive Care Units (ICU), derived from multi-centre eICU-CRD database and single-centre MIMIC-III database, spanning over 208 hospitals and 335 ICUs. Using heart rate, SaO2, respiratory rate, systolic, diastolic, and mean blood pressure, we develop machine learning models based on Logistic Regression and eXtreme Gradient Boosting and investigate their performance in predicting patients’ self-reported race. To balance the dataset between the three ethno-races considered in our study, we use a matching cohort based on age, gender, and admission diagnosis.
Findings standard machine learning models, derived solely on six vital signs can be used to predict patients’ self-reported race with AUC of 75%. Our findings hold under diverse patient populations, derived from multiple hospitals and intensive care units. We also show that oxygen saturation is a highly predictive variable, even when measured through methods other than pulse oximetry, namely arterial blood gas analysis, suggesting that addressing bias in routinely collected clinical variables will be challenging.
Interpretation our finding that machine learning models can predict self-reported race using solely vital signs creates a significant risk in clinical decision making, further exacerbating racial inequalities, with highly challenging mitigation measures
Investigating Presence of Ethnoracial Bias in Clinical Data using Machine Learning
No full textAn important target for machine learning research is obtaining unbiased results, which require addressing bias that might be present in the data as well as the methodology. This is of utmost importance in medical applications of machine learning, where trained models should be unbiased so as to result in systems that are widely applicable, reliable and fair. Since bias can sometimes be introduced through the data itself, in this paper we investigate the presence of ethnoracial bias in patients' clinical data. We focus primarily on vital signs and demographic information and classify patient ethnoraces in subsets of two from the three ethnoracial groups (African Americans, Caucasians, and Hispanics). Our results show that ethnorace can be identified in two out of three patients, setting the initial base for further investigation of the complex issue of ehtnoracial bias
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe 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
Red cell distribution width improves the simplified acute physiology score for risk prediction in unselected critically ill patients
Full text linkIntroduction: Recently, red cell distribution width (RDW), a measure of erythrocyte size variability, has been shown to be a prognostic marker in critical illness. The aim of this study was to investigate whether adding RDW has the potential to improve the prognostic performance of the simplified acute physiology score (SAPS) to predict short- and long-term mortality in an independent, large, and unselected population of intensive care unit (ICU) patients. Methods: This observational cohort study includes 17,922 ICU patients with available RDW measurements from different types of ICUs. We modeled the association between RDW and mortality by using multivariable logistic regression, adjusting for demographic factors, comorbidities, hematocrit, and severity of illness by using the SAPS. Results: ICU-, in-hospital-, and 1-year mortality rates in the 17,922 included patients were 7.6% (95% CI, 7.2 to 8.0), 11.2% (95% CI, 10.8 to 11.7), and 25.4% (95% CI, 24.8 to 26.1). RDW was significantly associated with in-hospital mortality (OR per 1% increase in RDW (95%CI)) (1.14 (1.08 to 1.19), P < 0.0001), ICU mortality (1.10 (1.06 to 1.15), P < 0.0001), and 1-year mortality (1.20 (95% CI, 1.14 to 1.26); P < 0.001). Adding RDW to SAPS significantly improved the AUC from 0.746 to 0.774 (P < 0.001) for in-hospital mortality and 0.793 to 0.805 (P < 0.001) for ICU mortality. Significant improvements in classification of SAPS were confirmed in reclassification analyses. Subgroups demonstrated robust results for gender, age categories, SAPS categories, anemia, hematocrit categories, and renal failure. Conclusions: RDW is a promising independent short- and long-term prognostic marker in ICU patients and significantly improves risk stratification of SAPS. Further research is needed the better to understand the pathophysiology underlying these effects.Swiss National Science Foundation (SNF PBBSP3-128266)Universität BaselNational Institute of Biomedical Imaging and Bioengineering (U.S.) (grant R01 EB001659)Robert Wood Johnson Foundation (Physician Faculty Scholars program, grant 66350
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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