To assess the outcomes and failure risk factors for Kahook Dual Blade (KDB) excisional goniotomy with cataract surgery (phaco-KDB) in eyes with various glaucoma subtypes and severities.
This ...multisurgeon consecutive case series included glaucomatous eyes with cataract that underwent phaco-KDB and had a minimum follow-up of 12 months postoperatively. Efficacy was assessed by absolute and qualified surgical success (defined by different criteria) and changes in intraocular pressure (IOP) and antiglaucoma medication (AGM) at the last postoperative follow-up. Safety included best-corrected visual acuity, cup-to-disc ratio, visual field mean deviation, retinal nerve fibre layer thickness, and adverse events.
A total of 108 eyes of 89 patients with a median follow-up of 18 months (range, 12–47 months) were included. IOP decreased by 26% from 19.1 ± 5.0 mm Hg to 14.1 ± 3.5 mm Hg (p < 0.001), AGM use decreased by 29% from 2.4 ± 1.3 medications to 1.7 ± 1.3 (p < 0.001), and 25% of eyes became free of AGMs (vs 3% at baseline). Qualified success rates achieved for IOP cutoffs of 18, 15, and 12 mm Hg were 87%, 68%, and 46%, respectively. Higher baseline IOP and postoperative incidence of IOP spikes were associated with a higher risk of surgical failure. Best-corrected visual acuity improved postoperatively (p < 0.001), and visual field mean deviation, cup-to-disc ratio, and retinal nerve fibre layer thickness remained stable. Overall, safety was favourable, and adverse events were transient and not sight threatening.
This multicentre Canadian study provides real-world data that support the safety and efficacy of phaco-KDB in reducing IOP and AGM use with no evidence of disease progression during the follow-up period.
El parámetro, cuya estimación y validación es central en la inferencia estadística, aparenta simplicidad en el discurso escolar, pero oculta una complejidad no atendida. Esta investigación busca dar ...evidencia de este problema desde la perspectiva didáctica de los libros de texto con relación a la articulación de los diferentes significados que se le da al parámetro a lo largo de los contenidos de un curso introductorio de probabilidad y estadística universitario. Los resultados muestran algunas inconsistencias e incluso la omisión de una conceptualización clara del parámetro en el discurso de los libros de texto, así como que su simplicidad es solo hecho aparente.
La Sexta Vera, Paula; Cossia, Lautaro
Perspectivas revista de ciencias sociales,
07/2020, Letnik:
5, Številka:
9
Journal Article
Odprti dostop
El proceso de transformación urbana del barrio República de la Sexta está generando cambios materiales y simbólicos que impactan de diversa manera en los hábitos, percepciones y expectativas de sus ...vecinos. En esa coyuntura, el Proyecto de Extensión Universitaria se propone trabajar con les jóvenes del barrio, quienes atraviesan un proceso de cambios que afecta la morfología territorial, interpela la identidad y promueve la reconfiguración del vínculo trazado con las facultades e instituciones de la Universidad Nacional de Rosario que funcionan en la Ciudad Universitaria de Rosario (CUR), más conocida como La Siberia. En este artículo se pretende exponer el fundamento teórico metodológico del proyecto de extensión en curso titulado: “La Sexta: desde lejos no se ve”.
La Sexta Paula Vera; Paula Vera; Lautaro Cossia
Perspectivas revista de ciencias sociales,
07/2020
9
Journal Article
Odprti dostop
El proceso de transformación urbana del barrio República de la Sexta está generando cambios materiales y simbólicos que impactan de diversa manera en los hábitos, percepciones y expectativas de sus ...vecinos. En esa coyuntura, el Proyecto de Extensión Universitaria se propone trabajar con les jóvenes del barrio, quienes atraviesan un proceso de cambios que afecta la morfología territorial, interpela la identidad y promueve la reconfiguración del vínculo trazado con las facultades e instituciones de la Universidad Nacional de Rosario que funcionan en la Ciudad Universitaria de Rosario (CUR), más conocida como La Siberia. En este artículo se pretende exponer el fundamento teórico metodológico del proyecto de extensión en curso titulado: “La Sexta: desde lejos no se ve”.
Sentiment Classification is a fundamental task in the field of Natural Language Processing, and has very important academic and commercial applications. It aims to automatically predict the degree of ...sentiment present in a text that contains opinions and subjectivity at some level, like product and movie reviews, or tweets. This can be really difficult to accomplish, in part, because different domains of text contains different words and expressions. In addition, this difficulty increases when text is written in a non-English language due to the lack of databases and resources. As a consequence, several cross-domain and cross-language techniques are often applied to this task in order to improve the results. In this work we perform a study on the ability of a classification system trained with a large database of product reviews to generalize to different Spanish domains. Reviews were collected from the MercadoLibre website from seven Latin American countries, allowing the creation of a large and balanced dataset. Results suggest that generalization across domains is feasible though very challenging when trained with these product reviews, and can be improved by pre-training and fine-tuning the classification model.
This paper deals with the convergence analysis of the SUCPA (Semi Unsupervised Calibration through Prior Adaptation) algorithm, defined from a first-order non-linear difference equations, first ...developed to correct the scores output by a supervised machine learning classifier. The convergence analysis is addressed as a dynamical system problem, by studying the local and global stability of the nonlinear map derived from the algorithm. This map, which is defined by a composition of exponential and rational functions, turns out to be non-hyperbolic with a non-bounded set of non-isolated fixed points. Hence, a non-standard method for solving the convergence analysis is used consisting of an ad-hoc geometrical approach. For a binary classification problem (two-dimensional map), we rigorously prove that the map is globally asymptotically stable. Numerical experiments on real-world application are performed to support the theoretical results by means of two different classification problems: Sentiment Polarity performed with a Large Language Model and Cat-Dog Image classification. For a greater number of classes, the numerical evidence shows the same behavior of the algorithm, and this is illustrated with a Natural Language Inference example. The experiment codes are publicly accessible online at the following repository: https://github.com/LautaroEst/sucpa-convergence
A wide variety of natural language tasks are currently being addressed with large-scale language models (LLMs). These models are usually trained with a very large amount of unsupervised text data and ...adapted to perform a downstream natural language task using methods like fine-tuning, calibration or in-context learning. In this work, we propose an approach to adapt the prior class distribution to perform text classification tasks without the need for labelled samples and only few in-domain sample queries. The proposed approach treats the LLM as a black box, adding a stage where the model posteriors are calibrated to the task. Results show that these methods outperform the un-adapted model for different number of training shots in the prompt and a previous approach were calibration is performed without using any adaptation data.
Sentiment Classification is a fundamental task in the field of Natural Language Processing, and has very important academic and commercial applications. It aims to automatically predict the degree of ...sentiment present in a text that contains opinions and subjectivity at some level, like product and movie reviews, or tweets. This can be really difficult to accomplish, in part, because different domains of text contains different words and expressions. In addition, this difficulty increases when text is written in a non-English language due to the lack of databases and resources. As a consequence, several cross-domain and cross-language techniques are often applied to this task in order to improve the results. In this work we perform a study on the ability of a classification system trained with a large database of product reviews to generalize to different Spanish domains. Reviews were collected from the MercadoLibre website from seven Latin American countries, allowing the creation of a large and balanced dataset. Results suggest that generalization across domains is feasible though very challenging when trained with these product reviews, and can be improved by pre-training and fine-tuning the classification model.
PDF
