3 research outputs found

    Investigación sobre el reconocimiento automático foliar de la laurisilva canaria

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    This paper summarizes a research that establishes the possibility of an automatic successful taxonomic task, for laurisilvae canariensis trees, using simple leaves. Images of scanned leaves are used as means of classification. These images are processed obtaining discriminating parameters that, by means of automatic learning models, are identified as descriptors of class belonging. The obtained result is weighted with an evidence value, a measure that allows for valuation of parameters or parameters set impact on the classifying task. The system may be used as mean of taxonomic parameter identification, allowing for a registration board or a plant patent chart setting, of developed or discovered valuable species.La investigación que se resume en el presente artículo, da evidencia de la posibilidad de lograr exitosamente una taxonomía de laurásea canaria de forma automática, usando muestras de hojas. Imágenes digitalizadas por medio de un escáner, de un muestrario de hojas, son usadas como elementos de clasificación. Éstas son procesadas, extrayendo parámetros discriminantes que, por medio de diferentes modelos de aprendizaje automático, los identifican como descriptivos de pertenencia a una especie. El resultado se presenta con un valor de evidencia, medición que permite evaluar el peso de parámetros o conjuntos de parámetros en la clasificación. El sistema podría ser usado para determinar parámetros taxonómicos que permitan establecer un sistema de patentes de plantas desarrolladas o descubiertas, de interés económico

    Graphomotor and Handwriting Disabilities Rating Scale (GHDRS): Towards Complex and Objective Assessment

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    Successful acquisition of handwriting skills as a child can have important consequences for later education. Possible graphomotor or handwriting disabilities (GD and HD, respectively) could reduce quality of life but effective remediation depends on proper diagnosis. However, current approaches to diagnosis and assessment of GD and HD have several limitations and knowledge gaps, e.g. they are subjective, they do not facilitate identification of specific manifestations, etc. The aim of this work is to address the limitations of current approaches and introduce a new scale (GHDRS - Graphomotor and Handwriting Disabilities Rating Scale) that will enable experts to perform objective and complex computer-aided diagnosis and assessment of GD and HD. The scale supports quantification of 17 manifestations associated with the process/product of drawing/handwriting. In addition, we provide normative data for Czech children attending up to the fourth grade of a primary school. Finally, the whole methodology of GHDRS design is made maximally transparent so that it could be adapted for other languages

    Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

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    Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)
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