LNCS

A controller is a device that interacts with a plant. At each time point,it reads the plant’s state and issues commands with the goal that the plant oper-ates optimally. Constructing optimal controllers is a fundamental and challengingproblem. Machine learning techniques have recently been successfully applied totrain controllers, yet they have limitations. Learned controllers are monolithic andhard to reason about. In particular, it is difficult to add features without retraining,to guarantee any level of performance, and to achieve acceptable performancewhen encountering untrained scenarios. These limitations can be addressed bydeploying quantitative run-timeshieldsthat serve as a proxy for the controller.At each time point, the shield reads the command issued by the controller andmay choose to alter it before passing it on to the plant. We show how optimalshields that interfere as little as possible while guaranteeing a desired level ofcontroller performance, can be generated systematically and automatically usingreactive synthesis. First, we abstract the plant by building a stochastic model.Second, we consider the learned controller to be a black box. Third, we mea-surecontroller performanceandshield interferenceby two quantitative run-timemeasures that are formally defined using weighted automata. Then, the problemof constructing a shield that guarantees maximal performance with minimal inter-ference is the problem of finding an optimal strategy in a stochastic2-player game“controller versus shield” played on the abstract state space of the plant with aquantitative objective obtained from combining the performance and interferencemeasures. We illustrate the effectiveness of our approach by automatically con-structing lightweight shields for learned traffic-light controllers in various roadnetworks. The shields we generate avoid liveness bugs, improve controller per-formance in untrained and changing traffic situations, and add features to learnedcontrollers, such as giving priority to emergency vehicles

A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension

Aims The aim was to validate, update, and extend the Diamond-Forrester model for estimating the probability of obstructive coronary artery disease (CAD) in a contemporary cohort. Methods and results Prospectively collected data from 14 hospitals on patients with chest pain without a history of CAD and referred for conventional coronary angiography (CCA) were used. Primary outcome was obstructive CAD, defined as ≥50% stenosis in one or more vessels on CCA. The validity of the Diamond-Forrester model was assessed using calibration plots, calibration-in-the-large, and recalibration in logistic regression. The model was subsequently updated and extended by revising the predictive value of age, sex, and type of chest pain. Diagnostic performance was assessed by calculating the area under the receiver operating characteristic curve (c-statistic) and reclassification was determined. We included 2260 patients, of whom 1319 had obstructive CAD on CCA. Validation demonstrated an overestimation of the CAD probability, especially in women. The updated and extended models demonstrated a c-statistic of 0.79 (95% CI 0.77-0.81) and 0.82 (95% CI 0.80-0.84), respectively. Sixteen per cent of men and 64% of women were correctly reclassified. The predicted probability of obstructive CAD ranged from 10% for 50-year-old females with non-specific chest pain to 91% for 80-year-old males with typical chest pain. Predictions varied across hospitals due to differences in disease prevalence. Conclusion Our results suggest that the Diamond-Forrester model overestimates the probability of CAD especially in women. We updated the predictive effects of age, sex, type of chest pain, and hospital setting which improved model performance and we extended it to include patients of 70 years and olde

Cognitive Flexibility and Clinical Severity in Eating Disorders

OBJECTIVES: The aim of this study was to explore cognitive flexibility in a large dataset of people with Eating Disorders and Healthy Controls (HC) and to see how patient characteristics (body mass index [BMI] and length of illness) are related to this thinking style. METHODS: A dataset was constructed from our previous studies using a conceptual shift test--the Brixton Spatial Anticipation Test. 601 participants were included, 215 patients with Anorexia Nervosa (AN) (96 inpatients; 119 outpatients), 69 patients with Bulimia Nervosa (BN), 29 Eating Disorder Not Otherwise Specified (EDNOS), 72 in long-term recovery from AN (Rec AN) and a comparison group of 216 HC. RESULTS: The AN and EDNOS groups had significantly more errors than the other groups on the Brixton Test. In comparison to the HC group, the effect size decrement was large for AN patients receiving inpatient treatment and moderate for AN outpatients. CONCLUSIONS: These findings confirm that patients with AN have poor cognitive flexibility. Severity of illness measured by length of illness does not fully explain the lack of flexibility and supports the trait nature of inflexibility in people with AN

Incremental value of the CT coronary calcium score for the prediction of coronary artery disease

Objectives:: To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). Methods:: We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Results:: Re-analysing the variables used by Diamond & Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Conclusions:: Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up. © 2010 The Author(s)

Cumulative incidence and risk factors for cutaneous squamous-cell carcinoma metastases in organ transplant recipients: the SCOPE-ITSCC metastases study, a prospective multi-center study.

Solid organ transplant recipients (SOTRs) are believed to have an increased risk of metastatic cutaneous squamous-cell carcinoma (cSCC), but reliable data are lacking regarding the precise incidence and associated risk factors. In a prospective cohort study, including 19 specialist dermatology outpatient clinics in 15 countries, patient and tumor characteristics were collected using standardized questionnaires when SOTRs presented with a new cSCC. After a minimum of 2 years of follow-up, relevant data for all SOTRs were collected. Cumulative incidence of metastases was calculated by the Aalen-Johansen estimator. Fine and Gray models were used to assess multiple risk factors for metastases. Of 514 SOTRs who presented with 623 primary cSCCs, 37 developed metastases with a 2-year patient-based cumulative incidence of 6.2%. Risk factors for metastases included location in the head and neck area, local recurrence, size >2cm, clinical ulceration, poor differentiation grade, perineural invasion and deep invasion. A high-stage tumor that is also ulcerated showed the highest risk of metastasis, with a 2-year cumulative incidence of 46.2% (31.9% - 68.4%). SOTRs have a high risk of cSCC metastases and well-established clinical and histological risk factors have been confirmed. High-stage, ulcerated cSCCs have the highest risk of metastasis. [Abstract copyright: Copyright © 2024. Published by Elsevier Inc.

Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

Objectives To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations

The updated NICE guidelines: Cardiac CT as 1st line test for coronary artery disease

Purpose of Review Cost-effective care pathways are integral to delivering sustainable healthcare programmes. Due to the overestimation of coronary artery disease using traditional risk tables, non-invasive testing has been utilised to improve risk stratification and initiate appropriate management to reduce the dependence on invasive investigations. In line with recent technological improvements, cardiac CT is a modality that offers a detailed anatomical assessment of coronary artery disease comparable to invasive coronary angiography. Recent Findings The recent publication of the National Institute for Health and Care Excellences (NICE) Clinical Guideline 95 update assesses the performance and cost utility of different non-invasive imaging strategies in patients presenting with suspected anginal chest pain. The low cost and high sensitivity of cardiac CT makes it the non-invasive test of choice in the evaluation of stable angina. This has now been ratified in national guidelines with NICE recommending cardiac CT as the first-line investigation for all patients presenting with chest pain due to suspected coronary artery disease. Additionally, randomised controlled trials have demonstrated that cardiac CT improves diagnostic certainty when incorporated into chest pain pathways. Summary NICE recommend cardiac CT as the first-line test for the evaluation of stable coronary artery disease in chest pain pathways