Automated legal sensemaking: the centrality of relevance and intentionality

Introduction: In a perfect world, discovery would ideally be conducted by the senior litigator who is responsible for developing and fully understanding all nuances of their client’s legal strategy. Of course today we must deal with the explosion of electronically stored information (ESI) that never is less than tens-of-thousands of documents in small cases and now increasingly involves multi-million-document populations for internal corporate investigations and litigations. Therefore scalable processes and technologies are required as a substitute for the authority’s judgment. The approaches taken have typically either substituted large teams of surrogate human reviewers using vastly simplified issue coding reference materials or employed increasingly sophisticated computational resources with little focus on quality metrics to insure retrieval consistent with the legal goal. What is required is a system (people, process, and technology) that replicates and automates the senior litigator’s human judgment. In this paper we utilize 15 years of sensemaking research to establish the minimum acceptable basis for conducting a document review that meets the needs of a legal proceeding. There is no substitute for a rigorous characterization of the explicit and tacit goals of the senior litigator. Once a process has been established for capturing the authority’s relevance criteria, we argue that literal translation of requirements into technical specifications does not properly account for the activities or states-of-affairs of interest. Having only a data warehouse of written records, it is also necessary to discover the intentions of actors involved in textual communications. We present quantitative results for a process and technology approach that automates effective legal sensemaking

Oral squamous cell carcinoma: clinicopathological features from 346 cases from a single Oral Pathology service during an 8-year period

Epidemiological data from oral squamous cell carcinoma (OSCC) is mostly derived from North American, European and East Asian populations. OBJECTIVE: The aim of this study was to report the demographic and clinicopathological features from OSCC diagnosed in an Oral Pathology service in southeastern Brazil in an 8-year period. MATERIAL AND METHODS: All OSCC diagnosed from 2005 to 2012 were reviewed, including histological analysis of all hematoxylin and eosin stained slides and review of all demographic and clinical information from the laboratory records. RESULTS: A total of 346 OSCC was retrieved and males represented 67% of the sample. Mean age of the patients was 62.3 years-old and females were affected a decade older than males (

"Watch Me Grow- Electronic (WMG-E)" surveillance approach to identify and address child development, parental mental health, and psychosocial needs : study protocol

Background: The COVID-19 pandemic and the associated economic recession has increased parental psychosocial stress and mental health challenges. This has adversely impacted child development and wellbeing, particularly for children from priority populations (culturally and linguistically diverse (CALD) and rural/regional communities) who are at an already increased risk of health inequality. The increased mental health and psychosocial needs were compounded by the closure of in-person preventive and health promotion programs resulting in health organisations embracing technology and online services. Watch Me Grow- Electronic (WMG-E) – developmental surveillance platform- exemplifies one such service. WMG-E was developed to monitor child development and guide parents towards more detailed assessments when risk is identified. This Randomised Controlled Trial (RCT) aims to expand WMG-E as a digital navigation tool by also incorporating parents’ mental health and psychosocial needs. Children and families needing additional assessments and supports will be electronically directed to relevant resources in the ‘care-as-usual’ group. In contrast, the intervention group will receive continuity of care, with additional in-person assessment and ‘warm hand over’ by a ‘service navigator’ to ensure their needs are met. Methods: Using an RCT we will determine: (1) parental engagement with developmental surveillance; (2) access to services for those with mental health and social care needs; and (3) uptake of service recommendations. Three hundred parents/carers of children aged 6 months to 3 years (recruited from a culturally diverse, or rural/regional site) will be randomly allocated to the ‘care-as-usual’ or ‘intervention’ group. A mixed methods implementation evaluation will be completed, with semi-structured interviews to ascertain the acceptability, feasibility and impact of the WMG-E platform and service navigator. Conclusions: Using WMG-E is expected to: normalise and de-stigmatise mental health and psychosocial screening; increase parental engagement and service use; and result in the early identification and management of child developmental needs, parental mental health, and family psychosocial needs. If effective, digital solutions such as WMG-E to engage and empower parents alongside a service navigator for vulnerable families needing additional support, will have significant practice and policy implications in the pandemic/post pandemic period. Trial registration: The trial (Protocol No. 1.0, Version 3.1) was registered with ANZCTR (registration number: ACTRN12621000766819) on July 21st, 2021 and reporting of the trial results will be according to recommendations in the CONSORT Statement

Concert recording 2017-05-04b

[Track 1]. Fanfare for the common man / Aaron Copland -- [Track 2]. Bach Buch / J.S. Bach translated by Pann -- [Track 3]. Highway musings / Ryan Key -- [Track 4]. Serenade in D minor / Antonín Dvorák

Contradictory reasoning network:an EEG and FMRI study

Contradiction is a cornerstone of human rationality, essential for everyday life and communication. We investigated electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) in separate recording sessions during contradictory judgments, using a logical structure based on categorical propositions of the Aristotelian Square of Opposition (ASoO). The use of ASoO propositions, while controlling for potential linguistic or semantic confounds, enabled us to observe the spatial temporal unfolding of this contradictory reasoning. The processing started with the inversion of the logical operators corresponding to right middle frontal gyrus (rMFG-BA11) activation, followed by identification of contradictory statement associated with in the right inferior frontal gyrus (rIFG-BA47) activation. Right medial frontal gyrus (rMeFG, BA10) and anterior cingulate cortex (ACC, BA32) contributed to the later stages of process. We observed a correlation between the delayed latency of rBA11 response and the reaction time delay during inductive vs. deductive reasoning. This supports the notion that rBA11 is crucial for manipulating the logical operators. Slower processing time and stronger brain responses for inductive logic suggested that examples are easier to process than general principles and are more likely to simplify communication. © 2014 Porcaro et al

Cutaneous lymphoma international consortium study of outcome in advanced stages of mycosis fungoides and Sézary syndrome: effect of specific prognostic markers on survival and development of a prognostic model

Advanced-stage mycosis fungoides (MF; stage IIB to IV) and Sézary syndrome (SS) are aggressive lymphomas with a median survival of 1 to 5 years. Clinical management is stage based; however, there is wide range of outcome within stages. Published prognostic studies in MF/SS have been single-center trials. Because of the rarity of MF/SS, only a large collaboration would power a study to identify independent prognostic markers. PATIENTS AND METHODS: Literature review identified the following 10 candidate markers: stage, age, sex, cutaneous histologic features of folliculotropism, CD30 positivity, proliferation index, large-cell transformation, WBC/lymphocyte count, serum lactate dehydrogenase, and identical T-cell clone in blood and skin. Data were collected at specialist centers on patients diagnosed with advanced-stage MF/SS from 2007. Each parameter recorded at diagnosis was tested against overall survival (OS). RESULTS: Staging data on 1,275 patients with advanced MF/SS from 29 international sites were included for survival analysis. The median OS was 63 months, with 2- and 5-year survival rates of 77% and 52%, respectively. The median OS for patients with stage IIB disease was 68 months, but patients diagnosed with stage III disease had slightly improved survival compared with patients with stage IIB, although patients diagnosed with stage IV disease had significantly worse survival (48 months for stage IVA and 33 months for stage IVB). Of the 10 variables tested, four (stage IV, age > 60 years, large-cell transformation, and increased lactate dehydrogenase) were independent prognostic markers for a worse survival. Combining these four factors in a prognostic index model identified the following three risk groups across stages with significantly different 5-year survival rates: low risk (68%), intermediate risk (44%), and high risk (28%). CONCLUSION: To our knowledge, this study includes the largest cohort of patients with advanced-stage MF/SS and identifies markers with independent prognostic value, which, used together in a prognostic index, may be useful to stratify advanced-stage patients

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages