Beginning with the TMS 2024 Annual Meeting & Exhibition (TMS2024), the TMS Program Committee has developed a new timeline for proposing TMS Annual Meeting symposia. Symposium proposals will now be ...due Jan 31 of the preceding year--approximately two months earlier than previous years' deadlines. For TMS2024, a timeline has been established.
BackgroundConcomitant treatment with renin-angiotensin system inhibitors (ACEI/ARB), diuretics and non-steroidal anti-inflammatory drugs (NSAID) has been named as triple whammy (TW). This interaction ...can produce acute kidney injury (AKI).PurposeTo implement a strategy in order to avoid the development of AKI due to TW interaction.Material and methodsA so-called ‘Avoiding TW strategy’ was implemented including the following activities: a multidisciplinary group (nephrologists, general practitioners (GP) and clinical pharmacists (CP)) was established to design the strategy; evidence on TW interaction and AKI was assessed; criteria for selection of candidates for intervention was agreed (concomitant use of ACEI/ARB, diuretics and NSAID); CP presented the programme to GPs; patients who were candidates for intervention were retrieved through an in-house developed software (OBSERVA) integrated in electronic clinical records in our region; a deprescription proposal was included in all retrieved clinical records with information about the risk of developing AKI due to the combination, suggesting the doctor to withdraw the NSAID and, if this was not possible, monitoring renal function and serum potassium levels was recommended; and valuation of NSAID withdrawal was planned.ResultsThe TW optimisation strategy was created and 1699 proposals were sent in August 2018. NSAID deprescription proposals were distributed among the different groups: M01AE (propionic acid derivatives): 54.3%; M01AH (coxibs): 27.8%; M01AB (acetic acid derivatives): 15.0%; M01AC (oxicams): 2.7%; M01AG (fenamates): 0.1%; and M01AX (other NSAID): 0.1%.Preliminary results, 2 months after the implementation, showed that 15% of proposals were evaluated by GPs, with an acceptance rate of 82%.ConclusionPharmacological interactions must be considered even more when they cause important morbidity such as AKI.CP intervention through electronic clinical records optimises pharmacotherapy and may reduce adverse events and improve patients’ safety.References and/or acknowledgements1. García MDP, Sánchez JG, Laso E, et al. Analysis of a design to detect triple whammy in patients with digoxin therapy. https://ejhp.bmj.com/content/23/Suppl_1/A193.1No conflict of interest.
LUCK OF THE DRAW Grose, Thomas K
ASEE prism,
02/2019, Volume:
28, Issue:
6
Journal Article
The academic grant process, if not broken, has become highly inefficient. In the 1970s, 40 percent to 50 percent of applications were green-lighted, and it was relatively easy for agencies to pick ...and fund the best proposals. Today, less money and more applicants mean only 10 percent to 20 percent receive funding. Instead of a competition between best ideas, it's now a contest to see who can write the best proposals, says Carl Bergstrom, a University of Washington biologist. One easy fix would be to give agencies more money for grants, but that's a heavy lift politically.
NICE and NHS England are consulting on changes to the way new treatments are appraised so that patients get access to the most cost‐effective treatments much faster. This article summarises the main ...changes being planned.
Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes ...from them. However, the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs) with ground truths, which may have some undesirable effects, such as introducing redundant noise, mismatching with ground truths, and detracting from the robustness of detectors. In this article, we propose a novel anchor-free oriented proposal generator (AOPG) that abandons horizontal box-related operations from the network architecture. AOPG first produces coarse oriented boxes by a coarse location module (CLM) in an anchor-free manner and then refines them into high-quality oriented proposals. After AOPG, we apply a Fast Region-based Convolutional Neural Network (R-CNN) head to produce the final detection results. Furthermore, the shortage of large-scale datasets is also a hindrance to the development of oriented object detection. To alleviate the data insufficiency, we release a new dataset on the basis of our DIOR dataset and name it DIOR-R. Massive experiments demonstrate the effectiveness of AOPG. Particularly, without bells and whistles, we achieve the accuracy of 64.41%, 75.24%, and 96.22% mAP on the DIOR-R, DOTA, and HRSC2016 datasets, respectively. Code and models are available at https://github.com/jbwang1997/AOPG .
Recently, object proposal generation has shown value for various vision tasks, such as object detection, semantic instance segmentation, multi-label image classification, and weakly supervised ...learning, by hypothesizing object locations. We are motivated by the fact that many traditional proposal methods generate dense proposals to cover as many objects as possible but that i) they usually fail to rank these proposals properly and ii) the number of proposals is very large. For example, the well-known object proposal generation methods, Edge Boxes and Selective Search, can achieve high detection recall with thousands of proposals per image. But the large number of generated proposals makes subsequent analyses difficult due to the large number of false alarms and heavy computation load. To significantly reduce the number of proposals, we design a computationally lightweight neural network to refine the initial object proposals. The refinement consists of two parallel processes, re-ranking and box regression. The proposed network can share convolutional features with other high-level tasks by joint training, so the proposal refinement can be very fast. We show a joint training example of object detection in this paper. Extensive experiments demonstrate that our method can achieve state-of-the-art performance with a few proposals compared with some well-known proposal generation methods.
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