To evaluate outcome trends of neonates born very preterm in 11 high-income countries participating in the International Network for Evaluating Outcomes of neonates.
In a retrospective cohort study, ...we included 154 233 neonates admitted to 529 neonatal units between January 1, 2007, and December 31, 2015, at 240/7 to 316/7 weeks of gestational age and birth weight <1500 g. Composite outcomes were in-hospital mortality or any of severe neurologic injury, treated retinopathy of prematurity, and bronchopulmonary dysplasia (BPD); and same composite outcome excluding BPD. Secondary outcomes were mortality and individual morbidities. For each country, annual outcome trends and adjusted relative risks comparing epoch 2 (2012-2015) to epoch 1 (2007-2011) were analyzed.
For composite outcome including BPD, the trend decreased in Canada and Israel but increased in Australia and New Zealand, Japan, Spain, Sweden, and the United Kingdom. For composite outcome excluding BPD, the trend decreased in all countries except Spain, Sweden, Tuscany, and the United Kingdom. The risk of composite outcome was lower in epoch 2 than epoch 1 in Canada (adjusted relative risks 0.78; 95% CI 0.74-0.82) only. The risk of composite outcome excluding BPD was significantly lower in epoch 2 compared with epoch 1 in Australia and New Zealand, Canada, Finland, Japan, and Switzerland. Mortality rates reduced in most countries in epoch 2. BPD rates increased significantly in all countries except Canada, Israel, Finland, and Tuscany.
In most countries, mortality decreased whereas BPD increased for neonates born very preterm.
Resumen:
La soberanía alimentaria es el derecho colectivo a decidir sobre la producción, distribución y consumo de alimentos y promueve la generación de suministros alimenticios para el consumo local ...de tal forma que los consumidores queden resguardados de la volatilidad de los precios de mercados internacionales. En los pueblos indígenas esta tendencia resulta trascendental para hacer frente a condiciones de inequidad histórica que han impactado negativamente su salud pública. No obstante, es ignorada en algunos casos y en otros reemplazada por nociones centradas en la seguridad alimentaria, término alineado con políticas transnacionales derivadas del modelo económico dominante.
Objetivo:
analizar con tres comunidades indígenas de Colombia las perspectivas de soberanía alimentaria y su influencia en la salud.
Método:
investigación participativa basada en la comunidad, para recolectar datos mediante grupos de discusión, entrevistas y observación. La población de estudio fueron tres comunidades indígenas del sur de Colombia. La selección de participantes se realizó según su trayectoria en la comunidad.
Resultados:
las comunidades entienden la soberanía alimentaria como la conservación de semillas nativas y alimentos propios, vista como oportunidad para el cuidado de la salud. Su debilitamiento se relaciona con el desarrollo de enfermedades de las personas y de la madre tierra. Para su fortalecimiento identificaron el tul, el yatul y la chagra (huerta) que reafirman la unión familiar, contribuyen a la recuperación de modos de producción desde la sabiduría ancestral y se posicionan como alternativa para la sostenibilidad económica.
Conclusión:
la soberanía alimentaria conserva los saberes y prácticas tradicionales para una alimentación propia, debilitada por los sistemas agroindustriales. Es asumida como iniciativa local suscrita en un proyecto global de resistencia política y económica para la salud colectiva de los pueblos.
A rare clinical observation of death from prolonged uneven external irradiation due to the deliberate use of an ionizing radiation source for illegal purposes has been presented. The main ...difficulties of postmortem diagnosis of this type of radiation-induced injury, considering the features of histological examinations and special methods of retrospective dosimetric evaluations, have been identified.
Reply to Gupta-Wright et al Nel, Jeremy Stephen; Ive, Prudence; Lippincott, Christopher Kirk
Clinical infectious diseases,
01/2018, Letnik:
66, Številka:
1
Journal Article
Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would ...be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images.
Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.
In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging area under the receiver operating characteristics curve (AUC) range 0·91–0·99, CT chest imaging 0·87–0·96, and mammography 0·81). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index AUC 0·55, disease distribution 0·61, and breast density 0·61). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study.
The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging.
National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology
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