Relation between the Global Burden of Disease and Randomized Clinical Trials Conducted in Latin America Published in the Five Leading Medical Journals

Background: Since 1990 non communicable diseases and injuries account for the majority of death and disability-adjusted life years in Latin America. We analyzed the relationship between the global burden of disease and Randomized Clinical Trials (RCTs) conducted in Latin America that were published in the five leading medical journals.Methodology/Principal Findings: We included all RCTs in humans, exclusively conducted in Latin American countries, and published in any of the following journals: Annals of Internal Medicine, British Medical Journal, Journal of the American Medical Association, Lancet, and New England Journal of Medicine. We described the trials and reported the number of RCTs according to the main categories of the global burden of disease. Sixty-six RCTs were identified. Communicable diseases accounted for 38 (57%) reports. Maternal, perinatal, and nutritional conditions accounted for 19 (29%) trials. Non-communicable diseases represent 48% of the global burden of disease but only 14% of reported trials. No trial addressed injuries despite its 18% contribution to the burden of disease in 2000.Conclusions/Significance: A poor correlation between the burden of disease and RCTs publications was found. Non communicable diseases and injuries account for up to two thirds of the burden of disease in Latin America but these topics are seldom addressed in published RCTs in the selected sample of journals. Funding bodies of health research and editors should be aware of the increasing burden of non communicable diseases and injuries occurring in Latin America to ensure that this growing epidemic is not neglected in the research agenda and not affected by publication bias

A Randomized Trial of Prophylactic Antibiotics for Miscarriage Surgery.

BACKGROUND: Surgical intervention is needed in some cases of spontaneous abortion to remove retained products of conception. Antibiotic prophylaxis may reduce the risk of pelvic infection, which is an important complication of this surgery, particularly in low-resource countries. METHODS: We conducted a double-blind, placebo-controlled, randomized trial investigating whether antibiotic prophylaxis before surgery to complete a spontaneous abortion would reduce pelvic infection among women and adolescents in low-resource countries. We randomly assigned patients to a single preoperative dose of 400 mg of oral doxycycline and 400 mg of oral metronidazole or identical placebos. The primary outcome was pelvic infection within 14 days after surgery. Pelvic infection was defined by the presence of two or more of four clinical features (purulent vaginal discharge, pyrexia, uterine tenderness, and leukocytosis) or by the presence of one of these features and the clinically identified need to administer antibiotics. The definition of pelvic infection was changed before the unblinding of the data; the original strict definition was two or more of the clinical features, without reference to the administration of antibiotics. RESULTS: We enrolled 3412 patients in Malawi, Pakistan, Tanzania, and Uganda. A total of 1705 patients were assigned to receive antibiotics and 1707 to receive placebo. The risk of pelvic infection was 4.1% (68 of 1676 pregnancies) in the antibiotics group and 5.3% (90 of 1684 pregnancies) in the placebo group (risk ratio, 0.77; 95% confidence interval [CI], 0.56 to 1.04; P = 0.09). Pelvic infection according to original strict criteria was diagnosed in 1.5% (26 of 1700 pregnancies) and 2.6% (44 of 1704 pregnancies), respectively (risk ratio, 0.60; 95% CI, 0.37 to 0.96). There were no significant between-group differences in adverse events. CONCLUSIONS: Antibiotic prophylaxis before miscarriage surgery did not result in a significantly lower risk of pelvic infection, as defined by pragmatic broad criteria, than placebo. (Funded by the Medical Research Council and others; AIMS Current Controlled Trials number, ISRCTN97143849.)

Do high fetal catecholamine levels affect heart rate variability and tneconiutn passage during labour?

Objectives. To deternrine the relationship between Umbilical arterial catecholamine levels and fetal heart rate variability and meconium passage.Study design. A prospective descriptive study was perfonned. Umbilical artery catecholamine levels were measured in 55 newborns and correlated with fetal heart rate before delivery, Umbilical arterial pH, base excess and the presence of meconum-stained liquor.Results and conclusion. The range of catecholanrine levels was enonnous, with very high epinephrine or norepinephrine levels in several fetuses. We were unable to demonstrate an association between high catecholamine levels and the presence of nonnal fetal heart rate variability despite acidaemia. We postulate that high catecholamine levels may inhibit fetal meconiUITl passage

Extent, Awareness and Perception of Dissemination Bias in Qualitative Research: An Explorative Survey

BACKGROUND: Qualitative research findings are increasingly used to inform decision-making. Research has indicated that not all quantitative research on the effects of interventions is disseminated or published. The extent to which qualitative researchers also systematically underreport or fail to publish certain types of research findings, and the impact this may have, has received little attention. METHODS: A survey was delivered online to gather data regarding non-dissemination and dissemination bias in qualitative research. We invited relevant stakeholders through our professional networks, authors of qualitative research identified through a systematic literature search, and further via snowball sampling. RESULTS: 1032 people took part in the survey of whom 859 participants identified as researchers, 133 as editors and 682 as peer reviewers. 68.1% of the researchers said that they had conducted at least one qualitative study that they had not published in a peer-reviewed journal. The main reasons for non-dissemination were that a publication was still intended (35.7%), resource constraints (35.4%), and that the authors gave up after the paper was rejected by one or more journals (32.5%). A majority of the editors and peer reviewers "(strongly) agreed" that the main reasons for rejecting a manuscript of a qualitative study were inadequate study quality (59.5%; 68.5%) and inadequate reporting quality (59.1%; 57.5%). Of 800 respondents, 83.1% "(strongly) agreed" that non-dissemination and possible resulting dissemination bias might undermine the willingness of funders to support qualitative research. 72.6% and 71.2%, respectively, "(strongly) agreed" that non-dissemination might lead to inappropriate health policy and health care. CONCLUSIONS: The proportion of non-dissemination in qualitative research is substantial. Researchers, editors and peer reviewers play an important role in this. Non-dissemination and resulting dissemination bias may impact on health care research, practice and policy. More detailed investigations on patterns and causes of the non-dissemination of qualitative research are needed

WHO Statement on Caesarean Section Rates

In 1985 when a group of experts convened by the World Health Organization in Fortaleza, Brazil, met to discuss the appropriate technology for birth, they echoed what at that moment was considered an unjustified and remarkable increase of caesarean section (CS) rates worldwide.1 Based on the evidence available at that time, the experts in Fortaleza concluded: ‘there is no justification for any region to have a caesarean section rate higher than 10–15%’.1 Over the years, this quote has become ubiquitous in scientific literature, being interpreted as the ideal CS rate. Although this reference range was intended for ‘populations’, which are defined by geopolitical boundaries, in many instances it has been mistakenly used as the measurement for healthcare facilities regardless of their complexity or other characteristics. In addition to the case mix of the obstetric population served, the use of CS at healthcare facilities is also affected by factors such as their capacity to handle cases, availability of resource and the clinical management protocols used locally

Low-Dose CT Image Enhancement Using Deep Learning

The application of ionizing radiation for diagnostic imaging is common around the globe. However, the process of imaging, itself, remains to be a relatively hazardous operation. Therefore, it is preferable to use as low a dose of ionizing radiation as possible, particularly in computed tomography (CT) imaging systems, where multiple x-ray operations are performed for the reconstruction of slices of body tissues. A popular method for radiation dose reduction in CT imaging is known as the quarter-dose technique, which reduces the x-ray dose but can cause a loss of image sharpness. Since CT image reconstruction from directional x-rays is a nonlinear process, it is analytically difficult to correct the effect of dose reduction on image quality. Recent and popular deep-learning approaches provide an intriguing possibility of image enhancement for low-dose artifacts. Some recent works propose combinations of multiple deep-learning and classical methods for this purpose, which over-complicate the process. However, it is observed here that the straight utilization of the well-known U-NET provides very successful results for the correction of low-dose artifacts. Blind tests with actual radiologists reveal that the U-NET enhanced quarter-dose CT images not only provide an immense visual improvement over the low-dose versions, but also become diagnostically preferable images, even when compared to their full-dose CT versions

Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the CT scan of a person to be in the training set, while other images of the exact same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat-map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets.Comment: This version has been submitted to CAAI Transactions on Intelligence Technology. 202

Semiautomated text analytics for qualitative data synthesis.

Approaches to synthesizing qualitative data have, to date, largely focused on integrating the findings from published reports. However, developments in text mining software offer the potential for efficient analysis of large pooled primary qualitative datasets. This case study aimed to (a) provide a step-by-step guide to using one software application, Leximancer, and (b) interrogate opportunities and limitations of the software for qualitative data synthesis. We applied Leximancer v4.5 to a pool of five qualitative, UK-based studies on transportation such as walking, cycling, and driving, and displayed the findings of the automated content analysis as intertopic distance maps. Leximancer enabled us to "zoom out" to familiarize ourselves with, and gain a broad perspective of, the pooled data. It indicated which studies clustered around dominant topics such as "people." The software also enabled us to "zoom in" to narrow the perspective to specific subgroups and lines of enquiry. For example, "people" featured in men's and women's narratives but were talked about differently, with men mentioning "kids" and "old," whereas women mentioned "things" and "stuff." The approach provided us with a fresh lens for the initial inductive step in the analysis process and could guide further exploration. The limitations of using Leximancer were the substantial data preparation time involved and the contextual knowledge required from the researcher to turn lines of inquiry into meaningful insights. In summary, Leximancer is a useful tool for contributing to qualitative data synthesis, facilitating comprehensive and transparent data coding but can only inform, not replace, researcher-led interpretive work

Quality of care for pregnant women and newborns—the WHO vision

In 2015, as we review progress towards Millennium Development Goals (MDGs), despite significant progress in reduction of mortality, we still have unacceptably high numbers of maternal and newborn deaths globally. Efforts over the past decade to reduce adverse outcomes for pregnant women and newborns have been directed at increasing skilled birth attendance.1,2 This has resulted in higher rates of births in health facilities in all regions.3 The proportion of deliveries reportedly attended by skilled health personnel in developing countries rose from 56% in 1990 to 68% in 2012.4 With increasing utilisation of health services, a higher proportion of avoidable maternal and perinatal mortality and morbidity have moved to health facilities. In this context, poor quality of care (QoC) in many facilities becomes a paramount roadblock in our quest to end preventable mortality and morbidity