OBJECTIVE:Intracranial myeloid sarcomas (IMS) are rare central nervous system manifestations of malignant hematopoietic neoplasms of myeloid origin such as acute myeloid leukemia and chronic myeloid leukemia. Reported cases in the literature are limited to primarily case reports. We present a systematic review of this rare central nervous system tumor, characterizing the clinical presentation, tumor location, histopathology, and available treatment modalities. We correlate these variables with mortality, recurrence, and complications to suggest optimal management strategies for IMS. METHODS:A systematic literature search was performed across Ovid MEDLINE, Scopus, and Embase using 14 search terms in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This systematic review examines variables such as patient age, tumor location, size, presenting symptoms, treatment modality, extent of resection, and mortality. We performed descriptive analyses to identify bivariate associations between patient characteristics, treatment, and outcomes. RESULTS:The mean age at diagnosis was 34.8 years, and the most common etiology was acute myeloid leukemia (68.8%). The most common presenting symptoms were headache (45.5%), vision complaints (27.3%), and weakness/motor symptoms (21.2%). IMS were most commonly located in the temporal lobe (10.1%), cerebellum (10.1%), or falcine/parasagittal (10.1%) region. Patients who received radiotherapy (P < 0.001) or chemotherapy (P < 0.001) had lower rates of mortality versus those who did not. Surgical treatment and extent of resection were not significantly associated with mortality (P > 0.05). CONCLUSION:The use of adjuvant radiotherapy or chemotherapy for IMS significantly reduces mortality, confirming IMS as a cranial manifestation of a systemic disease. Although surgical treatment is indicated for histopathologic diagnosis and to relieve mass effect, the extent of resection does not predict overall survival.

Objective In 2018, cIMPACT-NOW update 3 concluded that WHO grade II/III IDH-wildtype diffuse astrocytomas that contain TERT promoter mutations, chromosome 7 gain/10 loss, and/or EGFR amplification, correspond to a WHO grade IV diagnosis and should be classified as Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV (DAG-G). We present a single-institution series of patients with DAG-G and IDH-mutant astrocytomas and compare their clinical, molecular, and radiographic characteristics. Methods Patient data was retrospectively extracted from the EMR for all patients undergoing surgical biopsy/resection of a diffuse astrocytoma at our institution from 2018 to 2020. Clinical presentation, molecular alterations, radiographic appearance, surgery, and survival were reviewed for each patient. Results Six DAG-G patients were identified in our cohort. All patients had diffuse disease, and presented with expansile, T2 hyperintense lesions with minimal enhancement. Compared to patients with classic IDH-mutant astrocytomas, mean age for DAG-G patients was older (68 vs 33 years, p < 0.0001), tumors were more diffuse ( p = 0.02), with patients more likely to present with focal deficits and receive a biopsy only ( p = 0.005). Overall survival was significantly shorter for DAG-G patients ( p = 0.03). Conclusion Patients with DAG-G are more likely to be older than typical IDH-mutant diffuse astrocytoma patients. They are more likely to present with tumors in a diffuse pattern with focal deficits. When such patients are encountered, prompt biopsy/resection to confirm the diagnosis and immediate initiation of adjuvant therapy is recommended, as the disease progression and overall prognosis is similar to glioblastoma.

Joint attention - the ability to purposefully coordinate attention with another agent, and mutually attend to the same thing -- is a critical component of human social cognition. In this paper, we ask whether joint attention can be useful as a mechanism for improving multi-agent coordination and social learning. We first develop deep reinforcement learning (RL) agents with a recurrent visual attention architecture. We then train agents to minimize the difference between the attention weights that they apply to the environment at each timestep, and the attention of other agents. Our results show that this joint attention incentive improves agents' ability to solve difficult coordination tasks, by reducing the exponential cost of exploring the joint multi-agent action space. Joint attention leads to higher performance than a competitive centralized critic baseline across multiple environments. Further, we show that joint attention enhances agents' ability to learn from experts present in their environment, even when completing hard exploration tasks that do not require coordination. Taken together, these findings suggest that joint attention may be a useful inductive bias for multi-agent learning.

We demonstrate that self-supervised language modeling applied to mathematical formulas enables logical reasoning. To measure the logical reasoning abilities of language models, we formulate several evaluation (downstream) tasks, such as inferring types, suggesting missing assumptions and completing equalities. For training language models for formal mathematics, we propose a novel skip-tree task. We find that models trained on the skip-tree task show surprisingly strong mathematical reasoning abilities, and outperform models trained on standard skip-sequence tasks. We also analyze the models' ability to formulate new conjectures by measuring how often the predictions are provable and useful in other proofs.

We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space. The set of rewrites (i.e. transformations) that can be successfully performed on a statement represents essential semantic features of the statement. We can compress this information by embedding the formula in a vector space, such that the vector associated with a statement can be used to predict whether a statement can be rewritten by other theorems. Predicting the embedding of a formula generated by some rewrite rule is naturally viewed as approximate reasoning in the latent space. In order to measure the effectiveness of this reasoning, we perform approximate deduction sequences in the latent space and use the resulting embedding to inform the semantic features of the corresponding formal statement (which is obtained by performing the corresponding rewrite sequence using real formulas). Our experiments show that graph neural networks can make non-trivial predictions about the rewrite-success of statements, even when they propagate predicted latent representations for several steps. Since our corpus of mathematical formulas includes a wide variety of mathematical disciplines, this experiment is a strong indicator for the feasibility of deduction in latent space in general.

Collaboration requires agents to align their goals on the fly. Underlying the human ability to align goals with other agents is their ability to predict the intentions of others and actively update their own plans. We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous. Starting with pretrained, single-agent point to point navigation policies and using noisy, high-dimensional sensor inputs like lidar, we first learn via self-supervision motion predictions of all agents on the team. Next, HPP uses the prediction models to propose and evaluate navigation subgoals for completing the rendezvous task without explicit communication among agents. We evaluate HPP in a suite of unseen environments, with increasing complexity and numbers of obstacles. We show that HPP outperforms alternative reinforcement learning, path planning, and heuristic-based baselines on challenging, unseen environments. Experiments in the real world demonstrate successful transfer of the prediction models from sim to real world without any additional fine-tuning. Altogether, HPP removes the need for a centralized operator in multiagent systems by combining model-based RL and inference methods, enabling agents to dynamically align plans.

We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning. We suggest several logical reasoning tasks that can be used to evaluate language models trained on formal mathematical statements, such as type inference, suggesting missing assumptions and completing equalities. To train language models for formal mathematics, we propose a novel skip-tree task. We find that models trained on the skip-tree task show surprisingly strong mathematical reasoning abilities, and outperform models trained on standard skip-sequence tasks. We also analyze the models' ability to formulate new conjectures by measuring how often the predictions are provable and useful in other proofs.

Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies. This paper provides a theoretical analysis of credit assignment in gradient-based Meta-RL. Building on the gained insights we develop a novel meta-learning algorithm that overcomes both the issue of poor credit assignment and previous difficulties in estimating meta-policy gradients. By controlling the statistical distance of both pre-adaptation and adapted policies during meta-policy search, the proposed algorithm endows efficient and stable meta-learning. Our approach leads to superior pre-adaptation policy behavior and consistently outperforms previous Meta-RL algorithms in sample-efficiency, wall-clock time, and asymptotic performance.

We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as buildorder selection or tactics. A centralized scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We present the first result of applying deep reinforcement learning techniques to training a modular agent with selfplay, achieving 92% or 86% win rates against the ”Harder” (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.

Imitation learning is a powerful paradigm for robot skill acquisition. However, obtaining demonstrations suitable for learning a policy that maps from raw pixels to actions can be challenging. In this paper we describe how consumer-grade Virtual Reality headsets and hand tracking hardware can be used to naturally teleoperate robots to perform complex tasks. We also describe how imitation learning can learn deep neural network policies (mapping from pixels to actions) that can acquire the demonstrated skills. Our experiments showcase the effectiveness of our approach for learning visuomotor skills.

This is a knowledge translation project to promote the uptake of best practices in end-of-life (EOL) care within the primary care setting in British Columbia (BC) through the use of tools embedded into electronic medical records (EMRs). The knowledge-to-action model is used to engage primary care providers in co-designing, adopting and evaluating the EOL care toolkit built for 3 EMRs. The toolkit has a set of EMR-specific data entry templates, query/report functions and access to additional downloadable resources. It is based on the EOL learning module designed and offered by the BC General Practice Services Committee's Practice Support Program to improve EOL care by primary care providers in the province. Our web-based distribution method allows providers to download and install the toolkit then take part to evaluate its use and impact. Initial feedback from phases 1-3 (of 4) has been favorable and has led to iterative improvements.

OBJECTIVE:The aim of this paper is to report on the use of the systematised nomenclature of medicine clinical terms (SNOMED CT) by providing an overview of published papers.METHODS:Published papers on SNOMED CT between 2001 and 2012 were identified using PubMed and Embase databases using the keywords 'systematised nomenclature of medicine' and 'SNOMED CT'. For each paper the following characteristics were retrieved: SNOMED CT focus category (ie, indeterminate, theoretical, pre-development/design, implementation and evaluation/commodity), usage category (eg, prospective content coverage, used to classify or code in a study), medical domain and country.RESULTS:Our search strategy identified 488 papers. A comparison between the papers published between 2001-6 and 2007-12 showed an increase in every SNOMED CT focus category. The number of papers classified as 'theoretical' increased from 46 to 78, 'pre-development/design' increased from 61 to 173 and 'implementation' increased from 10 to 34. Papers classified as 'evaluation/commodity' only started to appear from 2010.CONCLUSIONS:The majority of studies focused on 'theoretical' and 'pre-development/design'. This is still encouraging as SNOMED CT is being harmonized with other standardized terminologies and is being evaluated to determine the content coverage of local terms, which is usually one of the first steps towards adoption. Most implementations are not published in the scientific literature, requiring a look beyond the scientific literature to gain insights into SNOMED CT implementations.

You have accessJournal of UrologyTechnology & Instruments: Surgical Education & Skills Assessment/Ureteroscopy I1 Apr 2012860 ESSENTIAL COMPONENTS OF A VALIDATED ROBOTIC SURGERY TRAINING CURRICULUM Andrew Hung, Isuru Jayaratna, Kara Teruya, Dennis Lee, Mihir Desai, Inderbir Gill, and Alvin Goh Andrew HungAndrew Hung Los Angeles, CA More articles by this author , Isuru JayaratnaIsuru Jayaratna Los Angeles, CA More articles by this author , Kara TeruyaKara Teruya Los Angeles, CA More articles by this author , Dennis LeeDennis Lee Los Angeles, CA More articles by this author , Mihir DesaiMihir Desai Los Angeles, CA More articles by this author , Inderbir GillInderbir Gill Los Angeles, CA More articles by this author , and Alvin GohAlvin Goh Los Angeles, CA More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.953AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES As the popularity of robotic surgery in urology continues to increase, the training model for surgical skill acquisition must adapt accordingly. At present, no validated or standardized curriculum for robotic surgery exists. We evaluate the application of three standardized, validated training modalities and propose a novel inter-correlated robotic surgery curriculum. METHODS We assessed robotic surgical skills in a cross-section of urology residents and expert robotic surgeons (completed >30 robotic cases) using three standardized training modules. The components of our integrated robotic surgery curriculum include: 1) 4 structured inanimate tasks designed to evaluate key cognitive and motor skills known as the Fundamentals of Robotic Surgery (FRS), 2) 4 validated virtual reality exercises on the da Vinci Skills Simulator, and 3) a standardized robotic surgical task in a live porcine model to emulate a clinical task and evaluated with the validated Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. RESULTS A total of 49 participants completed the three training modalities. The training level and robotic experience of the urology trainees (n=38) and expert surgeons (n=11) are summarized in Table 1. Experts consistently outperformed trainees in each modality (p<0.001). Overall performance of inanimate tasks (FRS) significantly correlated with virtual reality robotic performance (r= -0.7, p<0.0001) and in-vivo robotic performance based on GEARS (r= -0.8, p<0.0001). Simulation performance and in-vivo tissue performance also demonstrated strong correlation (r= 0.6, p=0.0002). Demographic information and multi-modality performance comparisons Trainee Expert p value No. participants 38 11 Age (years) 31(28-41) 42(34-53) <0.0001 Years in practice 7(0-28) Post-graduate year of training 5(3-8) Robotic experience (years) 1(0-4) 5(2-9) <0.0001 Robotic experience (cases) 0(0-20) 300(30-2000) <0.0001 Inanimate FRS tasks (time in seconds plus penalty adjustment) 985(492-1347) 593(348-700) <0.0001 Robotic Simulator (average score) 73(56-92) 87(77-94) 0.0005 In-vivo task based on GEARS (total score) 21(16-32) 31(27-33) <0.0001 CONCLUSIONS Each training modality in this study has previously been validated. Our findings confirmed their construct validity. Furthermore, we demonstrated a significant performance correlation across virtual reality, inanimate, and in-vivo settings. This has not previously been shown with any robotic training modality, particularly involving an in-vivo model. Future studies are needed to demonstrate that robotic skills training translates to clinical excellence. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e351 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Andrew Hung Los Angeles, CA More articles by this author Isuru Jayaratna Los Angeles, CA More articles by this author Kara Teruya Los Angeles, CA More articles by this author Dennis Lee Los Angeles, CA More articles by this author Mihir Desai Los Angeles, CA More articles by this author Inderbir Gill Los Angeles, CA More articles by this author Alvin Goh Los Angeles, CA More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

The Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) has been designated as the recommended clinical reference terminology for use in clinical information systems around the world and is reported to be used in over 50 countries. However, there are still few implementation details. This study examined the implementation of SNOMED CT in terms of design, use and maintenance issues involved in 13 healthcare organisations across eight countries through a series of interviews with 14 individuals. While a great deal of effort has been spent on developing and refining SNOMED CT, there is still much work ahead to bring SNOMED CT into routine clinical use.

Purpose: To determine the changes each Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) release undergoes and the implications of those changes.Methods: (1) We reviewed the SNOMED CT Component History documentation and analyzed the Component History table in detail. (2) We outlined a list of semantic changes that are made to SNOMED CT concepts that are not recorded as part of the Component History mechanism. (3) We reviewed the SNOMED CT Concept Model mechanism and how it has changed since the July 2006 release. We applied the above methods to determine the impact they had on the Clinical Observations Recording and Encoding (CORE) Problem List published by the United States National Library of Medicine National Institutes of Health.Results: Of the 5182 concepts in the problem list subset, 2135 (41.2%) underwent some form of change in the form of (1) fully specified name and preferred term; (2) concept status; (3) primitive/fully defined status, defining attributes and normal forms; and/or (4) position in hierarchy (top-level hierarchy, supertypes and subtypes).Conclusions: In this paper, we have identified four types of changes that occur over time as new SNOMED CT releases are introduced. Keeping track of these changes is important as they are not well published and have an impact in patient case queries and the accuracy of patient records. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

Background Over the past decade there has been a growing body of literature on how the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) can be implemented and used in different clinical settings. Yet, for those charged with incorporating SNOMED CT into their organisation's clinical applications and vocabulary systems, there are few detailed encoding instructions and examples available to show how this can be done and the issues involved. This paper describes a heuristic method that can be used to encode clinical terms in SNOMED CT and an illustration of how it was applied to encode an existing palliative care dataset. Methods The encoding process involves: identifying input data items; cleaning the data items; encoding the cleaned data items; and exporting the encoded terms as output term sets. Four outputs are produced: the SNOMED CT reference set; interface terminology set; SNOMED CT extension set and unencodeable term set. Results The original palliative care database contained 211 data elements, 145 coded values and 37,248 free text values. We were able to encode ~84% of the terms, another ~8% require further encoding and verification while terms that had a frequency of fewer than five were not encoded (~7%). Conclusions From the pilot, it would seem our SNOMED CT encoding method has the potential to become a general purpose terminology encoding approach that can be used in different clinical systems.