Efecto de las intervenciones no invasivas en el bienestar y la calidad de vida de los pacientes con cáncer de pulmón: resultados de una revisión sistemática

Aim: To highlight the results of a systematic review on the effect of non-invasive interventions delivered to lung cancer patients in improving their well-being and quality of life. Métodos: Systematic review of clinical trials assessing non-invasive interventions in lung cancer patients. Exhaustive bibliographic search in the main databases, and selection of the retrieved records to identify relevant trials on the topic. Methodological quality assessment paying special attention in randomization and allocation concealment. Heterogeneity among interventions and outcomes assessed in the included studies forced to synthesize results narratively. Results: The review identifi ed 9 clinical trials of variable quality assessing nursing programmes interventions, a nutritional intervention, counselling, an exercise programme, and refl exology. Nursing programmes to manage breathlessness seemed to have beneficial effects, as well as a nursing led assessment and follow up programme. Some counselling sessions could provide patients with skills to face simptoms better. The way to implement those interventions remains unknown. Conclusions: Implemention of multidisciplinary programmes to manage patients with lung cancer should be seriously considered. There exists the need for higher quality studies, with clear designs and methodologies that lead to stronger conclusions

Efecto de las intervenciones no invasivas en el bienestar y la calidad de vida de los pacientes con cáncer de pulmón: resultados de una revisión sistemática

Aim: To highlight the results of a systematic review on the effect of non-invasive interventions delivered to lung cancer patients in improving their well-being and quality of life. Métodos: Systematic review of clinical trials assessing non-invasive interventions in lung cancer patients. Exhaustive bibliographic search in the main databases, and selection of the retrieved records to identify relevant trials on the topic. Methodological quality assessment paying special attention in randomization and allocation concealment. Heterogeneity among interventions and outcomes assessed in the included studies forced to synthesize results narratively. Results: The review identifi ed 9 clinical trials of variable quality assessing nursing programmes interventions, a nutritional intervention, counselling, an exercise programme, and refl exology. Nursing programmes to manage breathlessness seemed to have beneficial effects, as well as a nursing led assessment and follow up programme. Some counselling sessions could provide patients with skills to face simptoms better. The way to implement those interventions remains unknown. Conclusions: Implemention of multidisciplinary programmes to manage patients with lung cancer should be seriously considered. There exists the need for higher quality studies, with clear designs and methodologies that lead to stronger conclusions

Inmunoterapia anti-GD2 en pacientes con neuroblastoma de alto riesgo: actualización

Càncer en els infants; Immunoteràpia; Medicina basada en l'evidènciaCancer in children; Immunotherapy; Evidence-based medicineCáncer en los niños; Inmunoterapia; Medicina basada en la evidenciaL'any 2010 es va publicar, dins de la col·lecció “Informes de Evaluación de Tecnologías Sanitarias”, un informe sobre immunoteràpia anti-GD2 en pacients amb neuroblastoma d'alt risc. L'objectiu del present informe és actualitzar-ne l'anterior amb la nova evidència disponible sobre l'eficàcia i la seguretat dels anticossos monoclonals anti-GD2 en pacients amb neuroblastoma d'alt risc.In 2010, a report on anti-GD2 immunotherapy in patients with high-risk neuroblastoma was published in the "Sanitary Technology Assessment Reports" collection. The objective of the present report is to update the previous with the new evidence available on the efficacy and safety of anti-GD2 monoclonal antibodies in patients with high risk neuroblastoma.En 2010 se publicó, dentro de la colección "Informes de Evaluación de Tecnologías Sanitarias", un informe sobre inmunoterapia anti-GD2 en pacientes con neuroblastoma de alto riesgo. El objetivo del presente informe es actualizar el anterior con la nueva evidencia disponible sobre la eficacia y la seguridad de los anticuerpos monoclonales anti-GD2 en pacientes con neuroblastoma de alto riesgo

Diagnostic tools for alzheimer's disease dementia and other dementias: an overview of diagnostic test accuracy (DTA) systematic reviews

Background: Dementia includes a group of neurodegenerative disorders characterized by progressive loss of cognitive function and a decrease in the ability to perform activities of daily living. Systematic reviews of diagnostic test accuracy (DTA) focus on how well the index test detects patients with the disease in terms of figures such as sensitivity and specificity. Although DTA reviews about dementia are essential, at present there is no information about their quantity and quality. Methods: We searched for DTA reviews in MEDLINE (1966–2013), EMBASE (1980–2013), The Cochrane Library (from its inception until December 2013) and the Database of Abstracts of Reviews of Effects (DARE). Two reviewers independently assessed the methodological quality of the reviews using the AMSTAR measurement tool, and the quality of the reporting using the PRISMA checklist. We describe the main characteristics of these reviews, including basic characteristics, type of dementia, and diagnostic test evaluated, and we summarize the AMSTAR and PRISMA scores. Results: We selected 24 DTA systematic reviews. Only 10 reviews (41.6%), assessed the bias of included studies and few (33%) used this information to report the review results or to develop their conclusions Only one review (4%) reported all methodological items suggested by the PRISMA tool. Assessing methodology quality by means of the AMSTAR tool, we found that six DTA reviews (25%) pooled primary data with the aid of methods that are used for intervention reviews, such as Mantel-Haenszel and separate random-effects models (25%), while five reviews (20.8%) assessed publication bias by means of funnel plots and/or Egger’s Test. Conclusions: Our assessment of these DTA reviews reveals that their quality, both in terms of methodology and reporting, is far from optimal. Assessing the quality of diagnostic evidence is fundamental to determining the validity of the operating characteristics of the index test and its usefulness in specific settings. The development of high quality DTA systematic reviews about dementia continues to be a challeng

Dual-Branch CNNs for Vehicle Detection and Tracking on LiDAR Data

We present a novel vehicle detection and tracking system that works solely on 3D LiDAR information. Our approach segments vehicles using a dual-view representation of the 3D LiDAR point cloud on two independently trained convolutional neural networks, one for each view. A bounding box growing algorithm is applied to the fused output of the networks to properly enclose the segmented vehicles. Bounding boxes are grown using a probabilistic method that takes into account also occluded areas. The final vehicle bounding boxes act as observations for a multi-hypothesis tracking system which allows to estimate the position and velocity of the observed vehicles. We thoroughly evaluate our system on the KITTI benchmarks both for detection and tracking separately and show that our dual-branch classifier consistently outperforms previous single-branch approaches, improving or directly competing to other state of the art LiDAR-based methods.This work was supported in part by the EU Project LOGIMATIC under Grant H2020-Galileo-2015-1-687534, in part by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI under Grant MDM-2016-0656, in part by the ColRobTransp Project under Grant DPI2016-78957-RAEI/FEDER EU, in part by the EB-SLAM Project under Grant DPI2017-89564-P, and in part by the FPU Grant under Grant FPU15/04446

Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Vehicle detection and tracking is a core ingredient for developing autonomous driving applications in urban scenarios. Recent image-based Deep Learning (DL) techniques are obtaining breakthrough results in these perceptive tasks. However, DL research has not yet advanced much towards processing 3D point clouds from lidar range-finders. These sensors are very common in autonomous vehicles since, despite not providing as semantically rich information as images, their performance is more robust under harsh weather conditions than vision sensors. In this paper we present a full vehicle detection and tracking system that works with 3D lidar information only. Our detection step uses a Convolutional Neural Network (CNN) that receives as input a featured representation of the 3D information provided by a Velodyne HDL-64 sensor and returns a per-point classification of whether it belongs to a vehicle or not. The classified point cloud is then geometrically processed to generate observations for a multi-object tracking system implemented via a number of Multi-Hypothesis Extended Kalman Filters (MH-EKF) that estimate the position and velocity of the surrounding vehicles. The system is thoroughly evaluated on the KITTI tracking dataset, and we show the performance boost provided by our CNN-based vehicle detector over a standard geometric approach. Our lidar-based approach uses about a 4% of the data needed for an image-based detector with similarly competitive results.Peer ReviewedPostprint (author's final draft

Dual-branch CNNs for vehicle detection and tracking on LiDAR data

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We present a novel vehicle detection and tracking system that works solely on 3D LiDAR information. Our approach segments vehicles using a dual-view representation of the 3D LiDAR point cloud on two independently trained convolutional neural networks, one for each view. A bounding box growing algorithm is applied to the fused output of the networks to properly enclose the segmented vehicles. Bounding boxes are grown using a probabilistic method that takes into account also occluded areas. The final vehicle bounding boxes act as observations for a multi-hypothesis tracking system which allows to estimate the position and velocity of the observed vehicles. We thoroughly evaluate our system on the KITTI benchmarks both for detection and tracking separately and show that our dual-branch classifier consistently outperforms previous single-branch approaches, improving or directly competing to other state of the art LiDAR-based methods.This work was supported in part by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI under Grant MDM-2016-0656, in part by the ColRobTransp Project under Grant DPI2016-78957-RAEI/FEDER EU, in part by the EB-SLAM Project under Grant DPI2017-89564-P, and in part by the FPU Grant under Grant FPU15/04446. The Associate Editor for this article was Z. Duric. (Víctor Vaquero and Iván del Pino contributed equally to this work.) (Corresponding author: Víctor Vaquero.)Peer ReviewedPostprint (author's final draft

Full Time Domain EMI Measurement system applied to Railway emissions according to IEC 62236-3-1/EN 50121-3-1 standards

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper studies the advantages of applying time-domain based instrumentation to conduct electromagnetic interference emissions from rolling-stock. In IEC 62236-3-1 or EN 50121-3-1 standards, it is mandatory to measure the railway vehicle in static and in-motion conditions. When conventional frequency sweep instrumentation is employed, difficulties regarding ambient noise variation and the short-duration of worst-case emission modes take place. In Annex B of the standard, a test procedure is described to acquire the worst-case EMI, however, as it is explained at the paper the effective measured time at each frequency is only 0.08 ms in some frequency bands. Hence, multiple movements of the vehicle are needed increasing the uncertainty of the measured source and making difficult to distinguish vehicle EMI from background noise interference. To solve this problem, a Full-TDEMI measurement system is proposed with the availability to increase the effective measured time, reduced the ambient noise variation, the usage of multiple antennas at the same time and the possibility to discard transient interference that should not be evaluated. At the end of the paper, measurements carried out with the time-domain system are shown demonstrating the effectivity of the methodology. © 2018 IEEE.Peer ReviewedPostprint (author's final draft

Low resolution lidar-based multi object tracking for driving applications

The final publication is available at link.springer.comVehicle detection and tracking in real scenarios are key com- ponents to develop assisted and autonomous driving systems. Lidar sen- sors are specially suitable for this task, as they bring robustness to harsh weather conditions while providing accurate spatial information. How- ever, the resolution provided by point cloud data is very scarce in com- parison to camera images. In this work we explore the possibilities of Deep Learning (DL) methodologies applied to low resolution 3D lidar sensors such as the Velodyne VLP-16 (PUCK), in the context of vehicle detection and tracking. For this purpose we developed a lidar-based sys- tem that uses a Convolutional Neural Network (CNN), to perform point- wise vehicle detection using PUCK data, and Multi-Hypothesis Extended Kalman Filters (MH-EKF), to estimate the actual position and veloci- ties of the detected vehicles. Comparative studies between the proposed lower resolution (VLP-16) tracking system and a high-end system, using Velodyne HDL-64, were carried out on the Kitti Tracking Benchmark dataset. Moreover, to analyze the influence of the CNN-based vehicle detection approach, comparisons were also performed with respect to the geometric-only detector. The results demonstrate that the proposed low resolution Deep Learning architecture is able to successfully accom- plish the vehicle detection task, outperforming the geometric baseline approach. Moreover, it has been observed that our system achieves a similar tracking performance to the high-end HDL-64 sensor at close range. On the other hand, at long range, detection is limited to half the distance of the higher-end sensor.Peer ReviewedPostprint (author's final draft

The updating of clinical practice guidelines: insights from an international survey

BACKGROUND: Clinical practice guidelines (CPGs) have become increasingly popular, and the methodology to develop guidelines has evolved enormously. However, little attention has been given to the updating process, in contrast to the appraisal of the available literature. We conducted an international survey to identify current practices in CPG updating and explored the need to standardize and improve the methods. METHODS: We developed a questionnaire (28 items) based on a review of the existing literature about guideline updating and expert comments. We carried out the survey between March and July 2009, and it was sent by email to 106 institutions: 69 members of the Guidelines International Network who declared that they developed CPGs; 30 institutions included in the U.S. National Guideline Clearinghouse database that published more than 20 CPGs; and 7 institutions selected by an expert committee. RESULTS: Forty-four institutions answered the questionnaire (42% response rate). In the final analysis, 39 completed questionnaires were included. Thirty-six institutions (92%) reported that they update their guidelines. Thirty-one institutions (86%) have a formal procedure for updating their guidelines, and 19 (53%) have a formal procedure for deciding when a guideline becomes out of date. Institutions describe the process as moderately rigorous (36%) or acknowledge that it could certainly be more rigorous (36%). Twenty-two institutions (61%) alert guideline users on their website when a guideline is older than three to five years or when there is a risk of being outdated. Twenty-five institutions (64%) support the concept of "living guidelines," which are continuously monitored and updated. Eighteen institutions (46%) have plans to design a protocol to improve their guideline-updating process, and 21 (54%) are willing to share resources with other organizations. CONCLUSIONS: Our study is the first to describe the process of updating CPGs among prominent guideline institutions across the world, providing a comprehensive picture of guideline updating. There is an urgent need to develop rigorous international standards for this process and to minimize duplication of effort internationally