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This groundbreaking work offers a first-of-its-kind overview of legal informatics, the academic discipline underlying the technological transformation and economics of the legal industry. Edited by Daniel Martin Katz, Ron Dolin, and Michael J. Bommarito, and featuring contributions from more than two dozen academic and industry experts, chapters cover the history and principles of legal informatics and background technical concepts – including natural language processing and distributed ledger technology. The volume also presents real-world case studies that offer important insights into document review, due diligence, compliance, case prediction, billing, negotiation and settlement, contracting, patent management, legal research, and online dispute resolution. Written for both technical and non-technical readers, Legal Informatics is the ideal resource for anyone interested in identifying, understanding, and executing opportunities in this exciting field.
Solutions like crowd screening and machine learning can assist systematic reviewers with heavy screening burdens but require training sets containing a mix of eligible and ineligible studies. This study explores using PubMed's Best Match algorithm to create small training sets containing at least five relevant studies.
Six systematic reviews were examined retrospectively. MEDLINE searches were converted and run in PubMed. The ranking of included studies was studied under both Best Match and Most Recent sort conditions.
Retrieval sizes for the systematic reviews ranged from 151 to 5,406 records and the numbers of relevant records ranged from 8 to 763. The median ranking of relevant records was higher in Best Match for all six reviews, when compared with Most Recent sort. Best Match placed a total of thirty relevant records in the first fifty, at least one for each systematic review. Most Recent sorting placed only ten relevant records in the first fifty. Best Match sorting outperformed Most Recent in all cases and placed five or more relevant records in the first fifty in three of six cases.
Using a predetermined set size such as fifty may not provide enough true positives for an effective systematic review training set. However, screening PubMed records ranked by Best Match and continuing until the desired number of true positives are identified is efficient and effective.
The Best Match sort in PubMed improves the ranking and increases the proportion of relevant records in the first fifty records relative to sorting by recency.
Autism spectrum disorders (ASD) are characterized by deficits in social interaction and behavioral impairments. Several studies have reported differences in white matter generalized Fractional Anisotropy (gFA) in ASD.
We studied white matter microstructural integrity in individuals with ASD.
We conducted the first DWI-based whole brain tractography study to compare gFA in 22 deep white matter tracts in first-degree relatives of individuals with ASD to controls and individuals with ASD. Futhermore, we replicated our significants results in an independant sample.
Fifty-one first-degree relatives of individuals with ASD, 29 controls and 14 individuals with ASD participated.
We performed q-ball imaging whole-brain tractography based on 1.5 T diffusion weighted MRI over 32 non-colinear directions. Then, we computed mean gFA along 22 main deep white matter tracts. A linear mixed model using group, gender, age and IQ as fixed effects and family as a random effect was used and Bonferroni correction applied. We also recruited a replication sample comprising 23 individuals with ASD and 32 controls.
We demonstrated a significantly reduced mean gFA along the left IFOF in first-degree relatives of individuals with ASD and individuals with ASD compared with controls and replicated this finding in an independant sample of patients. A decrease in mean gFA was also observed in the left CST when we compared first-degree relatives of individuals with ASD to controls (no such decrease was present in patients).
Our work suggests that structural fronto-occipital disconnectivity may be an endophenotype of ASD.
Although immune-mediated inflammatory diseases (IMID) are associated with multiple mental health conditions, there is a paucity of literature assessing personality disorders (PDs) in these populations. We aimed to estimate and compare the incidence of any PD in IMID and matched cohorts over time, and identify sociodemographic characteristics associated with the incidence of PD.
We used population-based administrative data from Manitoba, Canada to identify persons with incident inflammatory bowel disease (IBD), multiple sclerosis (MS) and rheumatoid arthritis (RA) using validated case definitions. Unaffected controls were matched 5:1 on sex, age and region of residence. PDs were identified using hospitalisation or physician claims. We used unadjusted and covariate-adjusted negative binomial regression to compare the incidence of PDs between the IMID and matched cohorts.
We identified 19 572 incident cases of IMID (IBD n = 6,119, MS n = 3,514, RA n = 10 206) and 97 727 matches overall. After covariate adjustment, the IMID cohort had an increased incidence of PDs (incidence rate ratio [IRR] 1.72; 95%CI: 1.47–2.01) as compared to the matched cohort, which remained consistent over time. The incidence of PDs was similarly elevated in IBD (IRR 2.19; 95%CI: 1.69–2.84), MS (IRR 1.79; 95%CI: 1.29–2.50) and RA (IRR 1.61; 95%CI: 1.29–1.99). Lower socioeconomic status and urban residence were associated with an increased incidence of PDs, whereas mid to older adulthood (age 45–64) was associated with overall decreased incidence. In a restricted sample with 5 years of data before and after IMID diagnosis, the incidence of PDs was also elevated before IMID diagnosis among all IMID groups relative to matched controls.
IMID are associated with an increased incidence of PDs both before and after an IMID diagnosis. These results support the relevance of shared risk factors in the co-occurrence of PDs and IMID conditions.
Viral pneumonia is an important cause of death and morbidity among infants worldwide. Transmission of non-influenza respiratory viruses in households can inform preventative interventions and has not been well-characterised in South Asia. From April 2011 to April 2012, household members of pregnant women enrolled in a randomised trial of influenza vaccine in rural Nepal were surveyed weekly for respiratory illness until 180 days after birth. Nasal swabs were tested by polymerase chain reaction for respiratory viruses in symptomatic individuals. A transmission event was defined as a secondary case of the same virus within 14 days of initial infection within a household. From 555 households, 825 initial viral illness episodes occurred, resulting in 79 transmission events. The overall incidence of transmission was 1.14 events per 100 person-weeks. Risk of transmission incidence was associated with an index case age 1–4 years (incidence rate ratio (IRR) 2.35; 95% confidence interval (CI) 1.40–3.96), coinfection as initial infection (IRR 1.94; 95% CI 1.05–3.61) and no electricity in household (IRR 2.70; 95% CI 1.41–5.00). Preventive interventions targeting preschool-age children in households in resource-limited settings may decrease the risk of transmission to vulnerable household members, such as young infants.
The current study examined whether self-reported memory problems among cognitively intact older adults changed concurrently with, preceded, or followed depressive symptoms over time.
Data were collected annually via in-person comprehensive medical and neuropsychological examinations as part of the Einstein Aging Study.
Community-dwelling older adults in an urban, multi-ethnic area of New York City were interviewed.
The current study included a total of 1,162 older adults (Mage = 77.65, SD = 5.03, 63.39% female; 74.12% White). Data were utilized from up to 11 annual waves per participant.
Multilevel modeling tested concurrent and lagged associations between three types of memory self-report (frequency of memory problems, perceived one-year decline, and perceived ten-year decline) and depressive symptoms.
Results showed that self-reported frequency of memory problems covaried with depressive symptoms only in participants who were older at baseline. Changes in perceived one-year and ten-year memory decline were related to changes in depressive symptoms across all ages. Depressive symptoms increased the likelihood of perceived ten-year memory decline the next year; however, perceived ten-year memory decline did not predict future depressive symptoms. Additionally, no significant temporal relationship was observed between depressive symptoms and self-reported frequency of memory problems or perceived one-year memory decline.
Our findings highlight the importance of testing the unique associations of different types of self-reported memory problems with depressive symptoms.
To determine infection prevention and control (IPAC) practices for carbapenemase-producing Enterobacteriaceae (CPE), an emerging threat, at acute-care hospitals in Ontario, Canada.
A descriptive cross-sectional survey.
We surveyed IPAC directors and managers at all acute-care hospitals in Ontario, Canada, to gather information on IPAC practices related to CPE, including admission screening, other patient screening, environmental testing, use of precautions to prevent transmission, and outbreak management.
Of 116 acute-care hospitals, 105 (91%) responded. Admission screening included patients previously colonized or infected with CPE (n = 64, 61%), patients recently hospitalized outside of Canada (Indian subcontinent, n = 62, 59%; other countries, n = 56, 53%), and patients recently hospitalized in Canada (n = 22, 21%). Fifty-one hospitals (49%) screened patients for colonization during an outbreak. Almost all hospitals (n = 101, 96%) used precautions to prevent transmission from patients with CPE colonization or infection; most hospitals (n = 54, 53%) continued precautions indefinitely. Few hospitals (n = 19, 18%) performed environmental cultures. Eight hospitals (8%) reported at least 1 outbreak, and 6 hospitals (6%) reported transmission from sink or shower drains to patients.
Variability in practices may result from lack of evidence and challenges in updating guidelines as evidence emerges. A coordinated approach to slow the emergence of CPE should be considered in our population.
Cyber Operational Risk: Cyber risk is routinely cited as one of the most important sources of operational risks facing organisations today, in various publications and surveys. Further, in recent years, cyber risk has entered the public conscience through highly publicised events involving affected UK organisations such as TalkTalk, Morrisons and the NHS. Regulators and legislators are increasing their focus on this topic, with General Data Protection Regulation (“GDPR”) a notable example of this. Risk actuaries and other risk management professionals at insurance companies therefore need to have a robust assessment of the potential losses stemming from cyber risk that their organisations may face. They should be able to do this as part of an overall risk management framework and be able to demonstrate this to stakeholders such as regulators and shareholders. Given that cyber risks are still very much new territory for insurers and there is no commonly accepted practice, this paper describes a proposed framework in which to perform such an assessment. As part of this, we leverage two existing frameworks – the Chief Risk Officer (“CRO”) Forum cyber incident taxonomy, and the National Institute of Standards and Technology (“NIST”) framework – to describe the taxonomy of a cyber incident, and the relevant cyber security and risk mitigation items for the incident in question, respectively.Summary of Results: Three detailed scenarios have been investigated by the working party:
∙ Employee leaks data at a general (non-life) insurer: Internal attack through social engineering, causing large compensation costs and regulatory fines, driving a 1 in 200 loss of £210.5m (c. 2% of annual revenue).
∙ Cyber extortion at a life insurer: External attack through social engineering, causing large business interruption and reputational damage, driving a 1 in 200 loss of £179.5m (c. 6% of annual revenue).
∙ Motor insurer telematics device hack: External attack through software vulnerabilities, causing large remediation / device replacement costs, driving a 1 in 200 loss of £70.0m (c. 18% of annual revenue).
Limitations: The following sets out key limitations of the work set out in this paper:
∙ While the presented scenarios are deemed material at this point in time, the threat landscape moves fast and could render specific narratives and calibrations obsolete within a short-time frame.
∙ There is a lack of historical data to base certain scenarios on and therefore a high level of subjectivity is used to calibrate them.
∙ No attempt has been made to make an allowance for seasonality of renewals (a cyber event coinciding with peak renewal season could exacerbate cost impacts)
∙ No consideration has been given to the impact of the event on the share price of the company.
∙ Correlation with other risk types has not been explicitly considered.
Conclusions: Cyber risk is a very real threat and should not be ignored or treated lightly in operational risk frameworks, as it has the potential to threaten the ongoing viability of an organisation. Risk managers and capital actuaries should be aware of the various sources of cyber risk and the potential impacts to ensure that the business is sufficiently prepared for such an event. When it comes to quantifying the impact of cyber risk on the operations of an insurer there are significant challenges. Not least that the threat landscape is ever changing and there is a lack of historical experience to base assumptions off. Given this uncertainty, this paper sets out a framework upon which readers can bring consistency to the way scenarios are developed over time. It provides a common taxonomy to ensure that key aspects of cyber risk are considered and sets out examples of how to implement the framework. It is critical that insurers endeavour to understand cyber risk better and look to refine assumptions over time as new information is received. In addition to ensuring that sufficient capital is being held for key operational risks, the investment in understanding cyber risk now will help to educate senior management and could have benefits through influencing internal cyber security capabilities.
To investigate whether amnestic mild cognitive impairment (aMCI) identified with visual memory tests conveys an increased risk of Alzheimer’s disease (risk-AD) and if the risk-AD differs from that associated with aMCI based on verbal memory tests.
4,771 participants aged 70.76 (SD = 6.74, 45.4% females) from five community-based studies, each a member of the international COSMIC consortium and from a different country, were classified as having normal cognition (NC) or one of visual, verbal, or combined (visual and verbal) aMCI using international criteria and followed for an average of 2.48 years. Hazard ratios (HR) and individual patient data (IPD) meta-analysis analyzed the risk-AD with age, sex, education, single/multiple domain aMCI, and Mini-Mental State Examination (MMSE) scores as covariates.
All aMCI groups (n = 760) had a greater risk-AD than NC (n = 4,011; HR range = 3.66 – 9.25). The risk-AD was not different between visual (n = 208, 17 converters) and verbal aMCI (n = 449, 29 converters, HR = 1.70, 95%CI: 0.88, 3.27, p = 0.111). Combined aMCI (n = 103, 12 converters, HR = 2.34, 95%CI: 1.13, 4.84, p = 0.023) had a higher risk-AD than verbal aMCI. Age and MMSE scores were related to the risk-AD. The IPD meta-analyses replicated these results, though with slightly lower HR estimates (HR range = 3.68, 7.43) for aMCI vs. NC.
Although verbal aMCI was most common, a significant proportion of participants had visual-only or combined visual and verbal aMCI. Compared with verbal aMCI, the risk-AD was the same for visual aMCI and higher for combined aMCI. Our results highlight the importance of including both verbal and visual memory tests in neuropsychological assessments to more reliably identify aMCI.
In “Toward a Theory of Race, Crime, and Urban Inequality,” Sampson and Wilson (1995) argued that racial disparities in violent crime are attributable in large part to the persistent structural disadvantages that are disproportionately concentrated in African American communities. They also argued that the ultimate causes of crime were similar for both Whites and Blacks, leading to what has been labeled the thesis of “racial invariance.” In light of the large scale social changes of the past two decades and the renewed political salience of race and crime in the United States, this paper reassesses and updates evidence evaluating the theory. In so doing, we clarify key concepts from the original thesis, delineate the proper context of validation, and address new challenges. Overall, we find that the accumulated empirical evidence provides broad but qualified support for the theoretical claims. We conclude by charting a dual path forward: an agenda for future research on the linkages between race and crime, and policy recommendations that align with the theory’s emphasis on neighborhood level structural forces but with causal space for cultural factors.
Objectives: The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup’s characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. Methods: We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. Results: The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. Conclusions: LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer’s disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 24, 511–523)
Magnetic field measurements in turbulent plasmas are often difficult to perform. Here we show that for
kG magnetic fields, a time-resolved Faraday rotation measurement can be made at the OMEGA laser facility. This diagnostic has been implemented using the Thomson scattering probe beam and the resultant path-integrated magnetic field has been compared with that of proton radiography. Accurate measurement of magnetic fields is essential for satisfying the scientific goals of many current laser–plasma experiments.
After the diagnosis of immune-mediated inflammatory diseases (IMID) such as inflammatory bowel disease (IBD), multiple sclerosis (MS) and rheumatoid arthritis (RA), the incidence of psychiatric comorbidity is increased relative to the general population. We aimed to determine whether the incidence of psychiatric disorders is increased in the 5 years before the diagnosis of IMID as compared with the general population.
Using population-based administrative health data from the Canadian province of Manitoba, we identified all persons with incident IBD, MS and RA between 1989 and 2012, and cohorts from the general population matched 5 : 1 on year of birth, sex and region to each disease cohort. We identified members of these groups with at least 5 years of residency before and after the IMID diagnosis date. We applied validated algorithms for depression, anxiety disorders, bipolar disorder, schizophrenia, and any psychiatric disorder to determine the annual incidence of these conditions in the 5-year periods before and after the diagnosis year.
We identified 12 141 incident cases of IMID (3766 IBD, 2190 MS, 6350 RA) and 65 424 matched individuals. As early as 5 years before diagnosis, the incidence of depression [incidence rate ratio (IRR) 1.54; 95% CI 1.30–1.84) and anxiety disorders (IRR 1.30; 95% CI 1.12–1.51) were elevated in the IMID cohort as compared with the matched cohort. Similar results were obtained for each of the IBD, MS and RA cohorts. The incidence of bipolar disorder was elevated beginning 3 years before IMID diagnosis (IRR 1.63; 95% CI 1.10–2.40).
The incidence of psychiatric comorbidity is elevated in the IMID population as compared with a matched population as early as 5 years before diagnosis. Future studies should elucidate whether this reflects shared risk factors for psychiatric disorders and IMID, a shared final common inflammatory pathway or other aetiology.
To describe the frequency, characteristics, and exposure associated with influenza in hospitalized patients in a Toronto hospital
Prospective data collected for consenting patients with laboratory-confirmed influenza and a retrospective review of infection control charts for roommates of cases over 3 influenza seasons
Of the 661 patients with influenza (age range: 1 week–103 years), 557 were placed on additional precautions upon admission. Of 104 with symptoms detected after admission, 57 cases were community onset and 47 were nosocomial (10 nosocomial were part of outbreaks). A total of 78 cases were detected after admission exposing 143 roommates. Among roommates tested for influenza after exposure, no roommates of community-onset cases and 2 of 16 roommates of nosocomial cases were diagnosed with influenza. Of 637 influenza-infected patients, 25% and 57% met influenza-like illness definitions from the Public Health Agency of Canada (PHAC) and Centers for Disease Control and Prevention (CDC), respectively, and 70.3% met the Provincial Infectious Diseases Advisory Committee (PIDAC) febrile respiratory illness definition. Among the 56 patients with community-onset influenza detected after admission, only 13%, 23%, and 34%, met PHAC, CDC, and PIDAC classifications, respectively.
In a setting with extensive screening and testing for influenza, 1 in 6 patients with influenza was not diagnosed until patients and healthcare workers had been exposed for >24 hours. Only 30% of patients with community-onset influenza detected after admission met the Ontario definition intended to identify cases, hampering efforts to prevent patient and healthcare worker exposures and reinforcing the need for prevention through vaccination.
Longitudinal administration of neuropsychological instruments are often used to assess age-related changes in cognition. Informative loss to follow-up may bias the results of these studies. Herein, we use auxiliary data to adjust for informative loss to follow-up. In the Einstein Aging Study, memory was assessed annually in a community sample of adults age 70+, free of dementia at baseline, using the free recall from the Free and Cued Selective Reminding Test, and via telephone using the Memory Impairment Screen for Telephone (the auxiliary data). Joint linear mixed models were used to assess how the effect of the APOE ε4 genotype may be affected by informative missingness in the in-person data. A total of 620 EAS participants contributed 2085 person years of follow-up to the analyses. Memory decline rates estimated in joint models were 19% greater in ε4 negative participants and 27% greater in ε4 positive participants compared to traditional approaches; the effect of APOE ε4 on memory decline was 37% greater. Joint modeling methods can help address bias caused by informative missing data in the estimation of the effect of risk factors on cognitive change, and may be applicable to a broader range of outcomes in longitudinal aging studies. (JINS, 2014, 20, 1–6)