1,720,982 research outputs found
Development of a patient-centered aggregate score to predict survival after lung resection for non–small cell lung cancer
No full textObjectiveThe objective of this analysis was to develop a survival aggregate score (SAS), including objective and subjective patient-based parameters, and assess its prognostic role after major anatomic resection for non–small cell lung cancer.MethodsA total of 245 patients underwent major lung resections for non–small cell lung cancer with preoperative evaluation of quality of life (Short-Form 36v2 survey) and complete follow-up. The Cox multivariable regression and bootstrap analyses were used to identify prognostic factors of overall servival, which were weighted to construct the scoring system and summed to generate the SAS.ResultsCox regression analysis showed that the factors negatively associated with overall survival and used to construct the score were 36-item short-form health survey physical component summary score less than 50 (hazard ratio [HR], 1.7; P = .008), aged older than 70 years (HR, 1.9; P = .002), and carbon monoxide lung diffusion capacity less than 70% (HR, 1.7; P = .01). Patients were grouped into 4 risk classes according to their SAS. The 5-year overall survival was 78% in class SAS0, 59% in class SAS1, 42% in class SAS2, and 14% in class SAS3 (log-rank test, P < .0001). SAS maintained its association with overall survival in patients with stages pT1 (log-rank test, P = .01), pT2 (log-rank test, P = .02), or pT3-4 (log-rank test, P = .001), and in those with stages pN0 (log-rank test, P = .0005) or pN1-2 (log-rank test, P = .02). The 5-year cancer-specific survival was 83% in class SAS0, 71% in class SAS1, 63% in class SAS2, and 17% in class SAS3 (log-rank test, P < .0001).ConclusionsThis system may be used to refine stratification of prognosis for clinical and research purposes
Preoperative maximum oxygen consumption is associated with prognosis after pulmonary resection in stage i non-small cell lung cancer
No full textA machine learning approach for postoperative outcome prediction: surgical data science application in a thoracic surgery setting
No full textBackground: The use of innovative methodologies, such as Surgical Data Science (SDS), based on artificial intelligence (AI) could prove to be useful for extracting knowledge from clinical data overcoming limitations inherent in medical registries analysis. The aim of the study is to verify if the application of an AI analysis to our database could develop a model able to predict cardiopulmonary complications in patients submitted to lung resection. Methods: We retrospectively analyzed data of patients submitted to lobectomy, bilobectomy, segmentectomy and pneumonectomy (January 2006–December 2018). Fifty preoperative characteristics were used for predicting the occurrence of cardiopulmonary complications. The prediction model was developed by training and testing a machine learning (ML) algorithm (XGBOOST) able to deal with registries characterized by missing data. We calculated the receiver operating characteristic curve, true positive rate (TPR), positive predictive value (PPV) and accuracy of the model. Results: We analyzed 1360 patients (lobectomy: 80.7%, segmentectomy: 11.9%, bilobectomy 3.7%, pneumonectomy: 3.7%) and 23.3% of them experienced cardiopulmonary complications. XGBOOST algorithm generated a model able to predict complications with an area under the curve of 0.75, a TPR of 0.76, a PPV of 0.68. The model’s accuracy was 0.70. The algorithm included all the variables in the model regardless of their completeness. Conclusions: Using SDS principles in thoracic surgery for the first time, we developed an ML model able to predict cardiopulmonary complications after lung resection based on 50 patient characteristics. The prediction was also possible even in the case of those patients for whom we had incomplete data. This model could improve the process of counseling and the perioperative management of lung resection candidates
Diagnostic role of tissue acquisition in patients with minimal fibrotic changes and normal lung functions
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A Machine Learning Approach for Postoperative Outcome Prediction: Surgical Data Science Application in a Thoracic Surgery Setting
No full textThe use of innovative methodologies, such as Surgical Data Science (SDS), based on artificial intelligence (AI) could prove to be useful for extracting knowledge from clinical data overcoming limitations inherent in medical registries analysis. The aim of the study is to verify if the application of an AI analysis to our database could develop a model able to predict cardiopulmonary complications in patients submitted to lung resection
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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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