Federico García Lorca y el cine

This article analyses Federico García Lorca's relationship with cinema, taking into account not only his screenplays but also his incorporation into his drama and poetic works of techniques borrowed from cinema

Federico García Lorca y el cine

This article analyses Federico García Lorca's relationship with cinema, taking into account not only his screenplays but also his incorporation into his drama and poetic works of techniques borrowed from cinema

Clowning and tragic clowning: Miguel de Unamuno as a funny writer

The present study considers the role and function that humour has in Unamuno’s intellectual and literary universe. It traces Unamuno’s attitude to humour to his reading of the Spanish character in En torno al casticismo (1895) and to his dialogue with the figure of Don Quixote, as found in Vida de Don Quijote y Sancho (1905) and Del sentimiento trágico de la vida (1912). Finally, it looks at the theory of humour offered in the novel Niebla and also at the role that humour played in Unamuno’s later political writings, especially those of exile (1924-1930)

El retablo de Maese Federico: Lorca’s Romancero gitano as puppet theatre

This article considers the profound influence that puppet theatre had on Federico García Lorca’s poetic vision and practice at the time that he was writing the poems that would eventually make up the Romancero gitano (1928). Taking the ‘Romance sonámbulo’ as its main example and focusing on elements such as setting, stage space, lighting and décor, characterisation, movement, choreography, and the complicit relationship between puppeteer, character and audience, it shows how Lorca draws on and plays with all the machinery and conventions of puppet theatre in this collection and, to all intents and purposes, transforms each of its poems into a mini puppet play. The article ends by considering the wider consequences for our reading of the Romancero gitano of Lorca’s puppet aesthetic

ARIA 2016: Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle

The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma a

In Search of a New Spanish Realism: Bardem's Calle Mayor (1956)

Non

In Search of a New Spanish Realism: Bardem's Calle Mayor (1956)

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A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio