The social psychology of seismic hazard adjustment: re-evaluating the international literature

The majority of people at risk from earthquakes do little or nothing to reduce their vulnerability. Over the past 40 years social scientists have tried to predict and explain levels of seismic hazard adjustment using models from behavioural sciences such as psychology. The present paper is the first to synthesise the major findings from the international literature on psychological correlates and causes of seismic adjustment at the level of the individual and the household. It starts by reviewing research on seismic risk perception. Next, it looks at norms and normative beliefs, focusing particularly on issues of earthquake protection responsibility and trust between risk stakeholders. It then considers research on attitudes towards seismic adjustment attributes, specifically beliefs about efficacy, control and fate. It concludes that an updated model of seismic adjustment must give the issues of norms, trust, power and identity a more prominent role. These have been only sparsely represented in the social psychological literature to date

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis

Silicon-based three-dimensional microstructures for radiation dosimetry in hadrontherapy

In this work, we propose a solid-state-detector for use in radiation microdosimetry. This device improves the performance of existing dosimeters using customized 3D-cylindrical microstructures etched inside silicon. The microdosimeter consists of an array of micro-sensors that have 3D-cylindrical electrodes of 15 μm diameter and a depth of 5 μm within a silicon membrane, resulting in a well-defined micrometric radiation sensitive volume. These microdetectors have been characterized using an 241Am source to assess their performance as radiation detectors in a high-LET environment. This letter demonstrates the capability of this microdetector to be used to measure dose and LET in hadrontherapy centers for treatment plan verification as part of their patient-specific quality control program

P145 DOES HEALTH-RELATED QUALITY OF LIFE CHANGE IN FOLLOW-UP IN ULCERATIVE COLITIS AND CROHN DISEASE?

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Trajectories of Maternal Mental Health: A Prospective Study of Mothers of Infants With Congenital Heart Defects From Pregnancy to 36 Months Postpartum

Objective To chart mothers' trajectories of mental health from pregnancy to 36 months postpartum in order to investigate the association between infants' congenital heart defects (CHD) and compromised maternal mental health. Methods Mothers of infants with mild, moderate, or severe CHD (n = 141) and mothers (n = 36,437) enrolled in the Norwegian Mother and Child Cohort Study were assessed at regular intervals from pregnancy up to 36 months postpartum, including measurements at 6 and 18 months, using an 8-item version of the Hopkins Symptom Checklist-25. Results Mean score trajectories of SCL-8 for mothers of infants with severe CHD deviated significantly from cohort controls 6, 18, and 36 months postpartum, indicating heightened symptoms of depression and anxiety. Conclusions Mothers of infants with severe CHD are at risk of compromised mental health from delivery to 36 months postpartum. Strain due to CHD-related interventions is identified as a possible partial mediator of the distres

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches

Relationship Satisfaction Among Mothers of Children With Congenital Heart Defects: A Prospective Case-Cohort Study

Objective To assess the level of partner relationship satisfaction among mothers of children with different severity of congenital heart defects (CHD) compared with mothers in the cohort. Methods Mothers of children with mild, moderate, or severe CHD (n = 182) and a cohort of mothers of children without CHD (n = 46,782) from the Norwegian Mother and Child Cohort Study were assessed at 5 time points from pregnancy to 36 months postpartum. A 5-item version of the Relationship Satisfaction scale was used, and relevant covariates were explored. Results The trajectories of relationship satisfaction among mothers of children with varying CHD severity did not differ from the trajectories in the cohort. All women in the cohort experienced decreasing relationship satisfaction from 18 months after delivery up to 36 months after delivery. Conclusions Having a child with CHD, regardless of severity, does not appear to exacerbate the decline in relationship satisfactio