NILC-Metrix : assessing the complexity of written and spoken language in Brazilian Portuguese

This paper presents and makes publicly available the NILC-Metrix, a computational system comprising 200 metrics proposed in studies on discourse, psycholinguistics, cognitive and computational linguistics, to assess textual complexity in Brazilian Portuguese (BP). These metrics are relevant for descriptive analysis and the creation of computational models and can be used to extract information from various linguistic levels of written and spoken language. The metrics in NILC-Metrix were developed during the last 13 years, starting in 2008 with Coh-Metrix-Port, a tool developed within the scope of the PorSimples project. Coh-Metrix-Port adapted some metrics to BP from the Coh-Metrix tool that computes metrics related to cohesion and coherence of texts in English. After the end of PorSimples in 2010, new metrics were added to the initial 48 metrics of Coh-Metrix-Port. Given the large number of metrics, we present them following an organisation similar to the metrics of Coh-Metrix v3.0 to facilitate comparisons made with metrics in Portuguese and English. In this paper, we illustrate the potential of NILC-Metrix by presenting three applications: (i) a descriptive analysis of the differences between children's film subtitles and texts written for Elementary School I and II (Final Years); (ii) a new predictor of textual complexity for the corpus of original and simplified texts of the PorSimples project; (iii) a complexity prediction model for school grades, using transcripts of children's story narratives told by teenagers. For each application, we evaluate which groups of metrics are more discriminative, showing their contribution for each task

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362