Phase transition in the Ising model on a small-world network with distance-dependent interactions

We study the collective behavior of an Ising system on a small-world network with the interaction $J(r) \propto r^{-\alpha}$, where $r$ represents the Euclidean distance between two nodes. In the case of $\alpha = 0$ corresponding to the uniform interaction, the system is known to possess a phase transition of the mean-field nature, while the system with the short-range interaction $(\alpha\to\infty)$ does not exhibit long-range order at any finite temperature. Monte Carlo simulations are performed at various values of $\alpha$, and the critical value $\alpha_c$ beyond which the long-range order does not emerge is estimated to be zero. Thus concluded is the absence of a phase transition in the system with the algebraically decaying interaction $r^{-\alpha}$ for any nonzero positive value of $\alpha$

Profiles in Parole Release and Revocation Maryland

The Maryland Parole Commission (MPC) was created in 1976 to replace the Board of Parole, which had been established in 1968. The first Advisory Board of Parole was founded in 1914. Maryland has had advisory sentencing guidelines since 1983. A sentence pronounced under the guidelines represents the maximum time an offender may serve and the parole commission then determines when an inmate will be considered for release

Perkins v Linkedin

ORDER GRANTING MOTION FOR FINAL APPROVAL OF SETTLEMENT AND GRANTING MOTION FOR ATTORNEY’S FEES, COSTS, AND REPRESENTATIVE PLAINTIFF AWARD

Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

Background - Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use this data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. Objective - In this paper, we discuss whether data that is recorded routinely as part of the health care process in primary care is actually fit to use for these other purposes, how the original purpose may affect the extent to which the data is fit for another purpose and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the (re-)use of this type of data. Methods –This discussion paper is based on the authors’ experience as users of electronic health records data, as a general practitioner, health informatics experts, and health services researchers. It is a product of the discussions they had during the TRANSFoRm project, which was funded by the EU and sought to develop, pilot and evaluate a core information architecture for the Learning Health System (LHS) in Europe, based on primary care electronic health records. Results – We first describe the different stages in the processing of EHR data, as well as the different purposes for which this data is used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step, that can generate biased outcomes. We identified thirteen possible sources of bias. Four of them are related to the organization of a health care system, some are of a more technical nature. Conclusions - There are a substantial number of possible sources of bias, and very little is known about the size and direction of their impact. However, any (re-)user of data that was recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor and purpose oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven healthcare will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda

RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19 Assessment in Primary Care

Clinical decision-making is a fundamental stage in delivering appropriate care to patients. In recent years several decision-making systems designed to aid the clinician in this process have been developed. However, technical solutions currently in use are based on simple regression models and are only able to take into account simple pre-defined multiple-choice features, such as patient age, pre-existing conditions, smoker status, etc. One particular source of patient data, that available decision-making systems are incapable of processing is the collection of patient consultation GP notes. These contain crucial signs and symptoms - the information used by clinicians in order to make a final decision and direct the patient to the appropriate care. Extracting information from GP notes is a technically challenging problem, as they tend to include abbreviations, typos, and incomplete sentences. This paper addresses this open challenge. We present a framework that performs knowledge graph construction from raw GP medical notes written during or after patient consultations. By relying on support phrases mined from the SNOMED ontology, as well as predefined supported facts from values used in the RECAP (REmote COVID-19 Assessment in Primary Care) patient risk prediction tool, our graph generative framework is able to extract structured knowledge graphs from the highly unstructured and inconsistent format that consultation notes are written in. Our knowledge graphs include information about existing patient symptoms, their duration, and their severity. We apply our framework to consultation notes of COVID-19 patients in the UK COVID-19 Clinical Assesment Servcie (CCAS) patient dataset. We provide a quantitative evaluation of the performance of our framework, demonstrating that our approach has better accuracy than traditional NLP methods when answering questions about patients

Early diagnostic suggestions improve accuracy of GPs:a randomised controlled trial using computer-simulated patients

Background: Designers of computerised diagnostic support systems (CDSSs) expect physicians to notice when they need advice and enter into the CDSS all information that they have gathered about the patient. The poor use of CDSSs and the tendency not to follow advice once a leading diagnosis emerges would question this expectation.Aim: To determine whether providing GPs with diagnoses to consider before they start testing hypotheses improves accuracy.Design and setting: Mixed factorial design, where 297 GPs diagnosed nine patient cases, differing in difficulty, in one of three experimental conditions: control, early support, or late support.Method: Data were collected over the internet. After reading some initial information about the patient and the reason for encounter, GPs requested further information for diagnosis and management. Those receiving early support were shown a list of possible diagnoses before gathering further information. In late support, GPs first gave a diagnosis and were then shown which other diagnoses they could still not discount.Results: Early support significantly improved diagnostic accuracy over control (odds ratio [OR] 1.31; 95% confidence interval [95%CI] = 1.03 to 1.66, P = 0.027), while late support did not (OR 1.10; 95% CI = 0.88 to 1.37). An absolute improvement of 6% with early support was obtained. There was no significant interaction with case difficulty and no effect of GP experience on accuracy. No differences in information search were detected between experimental conditions.Conclusion: Reminding GPs of diagnoses to consider before they start testing hypotheses can improve diagnostic accuracy irrespective of case difficulty, without lengthening information search

An instrument to identify computerised primary care research networks, genetic and disease registries prepared to conduct linked research:TRANSFoRm International Research Readiness (TIRRE) survey

PURPOSE: The Translational Research and Patients safety in Europe (TRANSFoRm) project aims to integrate primary care with clinical research whilst improving patient safety. The TRANSFoRm International Research Readiness survey (TIRRE) aims to demonstrate data use through two linked data studies and by identifying clinical data repositories and genetic databases or disease registries prepared to participate in linked research. METHOD: The TIRRE survey collects data at micro-, meso- and macro-levels of granularity; to fulfil data, study specific, business, geographical and readiness requirements of potential data providers for the TRANSFoRm demonstration studies. We used descriptive statistics to differentiate between demonstration-study compliant and non-compliant repositories. We only included surveys with >70% of questions answered in our final analysis, reporting the odds ratio (OR) of positive responses associated with a demonstration-study compliant data provider. RESULTS: We contacted 531 organisations within the Eurpean Union (EU). Two declined to supply information; 56 made a valid response and a further 26 made a partial response. Of the 56 valid responses, 29 were databases of primary care data, 12 were genetic databases and 15 were cancer registries. The demonstration compliant primary care sites made 2098 positive responses compared with 268 in non-use-case compliant data sources [OR: 4.59, 95% confidence interval (CI): 3.93–5.35, p < 0.008]; for genetic databases: 380:44 (OR: 6.13, 95% CI: 4.25–8.85, p < 0.008) and cancer registries: 553:44 (OR: 5.87, 95% CI: 4.13–8.34, p < 0.008). CONCLUSIONS: TIRRE comprehensively assesses the preparedness of data repositories to participate in specific research projects. Multiple contacts about hypothetical participation in research identified few potential sites