Notes

Lacking details are badly dealt with and also documented in conjecture style studies employing machine understanding: a novels evaluate.
79;
<.0001) and a moderate correlation for parents of boys aged <7years (r=0.64;
=.0007). There was a significant difference between the mean H-FIT scores for parents of boys using extended half-life factor (68.1; standard deviation [SD]=14.2) compared to standard half-life factor (54.7; SD=18.4;
=.04).

A novel, disease-specific tool, the H-FIT, has been developed to measure the impact of hemophilia on families. The H-FIT has good preliminary measurement properties and may be responsive to changes in therapy associated with a decreased burden of administration.
A novel, disease-specific tool, the H-FIT, has been developed to measure the impact of hemophilia on families. The H-FIT has good preliminary measurement properties and may be responsive to changes in therapy associated with a decreased burden of administration.
Randomized controlled trials on menopausal hormone therapy in humans have not confirmed the benefit of estrogens on cardiovascular disease found in animal studies. Flawed methodology or publication bias in animal studies may explain the dicrepancy.

The aim of this study was to investigate whether publication of the randomized controlled trials Heart and Estrogen/Progestin Replacement Study and Women's Health Initiative influenced study authors' assessment of research findings (confirmation bias) as well as to investigate publication bias and small-study effects in animal studies of estrogen effects on atherosclerosis.

The data source for this study was PubMed from inception to 2018. We selected animal studies with cardiovascular outcomes comparing 17-β-estradiol, its natural metabolites, or conjugated equine estrogen with control. Qualitative data were extracted on authors' conclusions about estrogen effects on cardiovascular disease, as well as quantitative data for atherosclerosis outcomes. Fixed- andEgger's regression suggested publication bias and/or exaggerated effects in small studies, which was more pronounced after 2002. There was substantial heterogeneity of effects (

=68%-86%) overall and in all subgroups except cynomolgus monkeys (

=9%), the only animal subgroup without clear bias. Adjusting for publication bias, overall estimates of estrogen effects on atherosclerosis were close to null effect.

We found substantial evidence of publication bias but not of confirmation bias. Publication bias and flawed small studies may partly explain why findings differ between animal studies and clinical trials on the effect of estrogens on cardiovascular disease.
We found substantial evidence of publication bias but not of confirmation bias. Publication bias and flawed small studies may partly explain why findings differ between animal studies and clinical trials on the effect of estrogens on cardiovascular disease.
Bleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection.

To develop a deep learning model that detects and visualizes bleeding events in electronic health records.

Three hundred electronic health records with
diagnosis codes for bleeding or leukemia were extracted. Alectinib Each sentence in the electronic health record was annotated as positive or negative for bleeding. The annotated sentences were used to develop a deep learning model that detects bleeding at sentence and note level.

On a balanced test set of 1178 sentences, the best-performing deep learning model achieved a sensitivity of 0.90, specificity of 0.90, and negative predictive value of 0.90. On a test set consisting of 700 notes, of which 49 were positive for bleeding, the model achieved a note-level sensitivity of 1.00, specificity of 0.52, and negative predictive value of 1.00. By using a sentence-level model on a note level, the model can explain its predictions by visualizing the exact sentence in a note that contains information regarding bleeding. Moreover, we found that the model performed consistently well across different types of bleedings.

A deep learning model can be used to detect and visualize bleeding events in the free text of electronic health records. The deep learning model can thus facilitate systematic assessment of bleeding risk, and thereby optimize patient care and safety.
A deep learning model can be used to detect and visualize bleeding events in the free text of electronic health records. The deep learning model can thus facilitate systematic assessment of bleeding risk, and thereby optimize patient care and safety.Many studies have focused on investigating deviations from additive interaction of two dichotomous risk factors on a binary outcome. There is, however, a gap in the literature with respect to interactions on the additive scale of >2 risk factors. In this paper, we present an approach for examining deviations from additive interaction among three or more binary exposures. The relative excess risk due to interaction (RERI) is used as measure of additive interaction. First, we concentrate on three risk factors - we propose to decompose the total RERI to the RERI owned to the joint presence of all three risk factors and the RERI of any two risk factors, given that the third is absent. We then extend this approach, to >3 binary risk factors. For illustration, we use a sample from data from the Greek EPIC cohort and we investigate the association with overall mortality of Mediterranean diet, body mass index , and smoking. Our formulae enable better interpretability of any evidence for deviations from additivity owned to more than two risk factors and provide simple ways of communicating such results from a public health perspective by attributing any excess relative risk to specific combinations of these factors. Abbreviations BMI Body Mass Index; ERR excess relative risk; EPIC European Prospective Investigation into Cancer and nutrition; MD Mediterranean diet; RERI relative excess risk due to interaction; RR relative risk; TotRERI total relative excess risk due to interaction.
This two-phase study seeks to contribute to research in the field of rural cancer health; specifically, the aim is to gain insight into the experiences of seeking, accessing and using information and health services throughout the cancer journey (diagnosis, treatment and follow-up care) for recently diagnosed (≤6 months) older patients (≥65 years) in rural areas.

Data will be collected through in-depth semi-structured interviews. In phase 1 (before 23
March 2020) interviews were conducted with healthcare professionals (HCP) to explore their experiences of delivering care to their elderly patients. In the second phase (starting January 2021) we will conduct interviews with cancer patients to understand the impact of COVID-19 and shielding on their experiences of being diagnosed, attending appointments and accessing and receiving support from community organisations and informal support from family and friends. Data gathered will be analysed using the Framework Method.

The study has been approved by the Health Research Authority and the United Lincolnshire Hospitals NHS Trust.
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