Colorectal cancer patients with EGFR-negative tumors have the potential to respond to cetuximab-based therapies. EGFR analysis by current IHC techniques does not seem to have predictive value, and selection or exclusion of patients for cetuximab therapy on the basis of currently available EGFR IHC does not seem warranted.

The relationship between season of the year and criminal behavior is a classical topic in criminological research. However, much of the research in this field is atheoretical and the findings are inconsistent and contradictory. The present study investigated the seasonality of homicide and robbery in Israel from 1977 to 1985. The authors' analysis was informed by the routine activity approach, which views crime as a function of three main elements: motivated offenders, suitable targets, and absence of guardians. Based on this approach and on the differences between homicide and robbery (with regard to motivation, victim-offender relationships, and planning), the authors hypothesized that robbery would show a seasonal trend and would peak during the winter (November through March) due to the increase in the cost of living and the facilitating environmental conditions during these months. In contrast, they expected homicide to be most prevalent in August (when social interaction is at its highest level), and that it would not follow a distinct seasonal pattern because victim availability and suitability, as well as the variety of motives involved in homicide, make this crime much less dependent on climatic conditions. The data were analyzed by three statistical methods: a stochastic model (SARIMA), a X - 11 seasonal adjustment, and a rank order of the three highest months. The results generally supported the hypotheses indicating the utility of the routine activity approach as a useful framework for analyzing the seasonality of crime. The methodological implications of using different definitions and measures of seasonality are discussed, and suggestions are put forward for further study in this field.

ObjectiveTo determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines.DesignA scoping review.Data sourcesMEDLINE, EMBASE, CINAHL, ProQuest, Scopus, Web of Science, Cochrane Library, INSPEC and ACM Digital Library were searched on 18 July 2018.Eligibility criteriaWe included English articles published between 1980 and 2018 that used machine learning to predict population-health-related outcomes. We excluded studies that only used logistic regression or were restricted to a clinical context.Data extraction and synthesisWe summarised findings extracted from published reports, which included general study characteristics, aspects of model development, reporting of results and model discussion items.ResultsOf 22 618 articles found by our search, 231 were included in the review. The USA (n=71, 30.74%) and China (n=40, 17.32%) produced the most studies. Cardiovascular disease (n=22, 9.52%) was the most studied outcome. The median number of observations was 5414 (IQR=16 543.5) and the median number of features was 17 (IQR=31). Health records (n=126, 54.5%) and investigator-generated data (n=86, 37.2%) were the most common data sources. Many studies did not incorporate recommended guidelines on machine learning and predictive modelling. Predictive discrimination was commonly assessed using area under the receiver operator curve (n=98, 42.42%) and calibration was rarely assessed (n=22, 9.52%).ConclusionsMachine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve machine learning applications in population health.Registration numberRegistered on the Open Science Framework on 17 July 2018 (available at https://osf.io/rnqe6/).

Sometimes mothers felt exposed to more blame than fathers, sometimes family members avoided troubled children (''courtesy stigma''), and child troubles were often perceived by interviewees to have disconnected or disrupted their social networks. In addition, Francis extends her discussion of stigma to describe how the age of the child and the child's problem accounted for variation in the experience of stigma. For example, parents of children with uncontested physical disabilities were unlikely to report feeling that others blamed them for their child's condition, in contrast to parents of children with alcohol or drug problems. As the contrast between these two conditions represents the distinction between disability and deviance, this is consistent with other research.The effect on sense of self and well-being is the subject of the final two chapters. Here again gender plays an important role in understanding differences in parents' experiences, with mothers being more likely to express feelings of guilt about their children's problems and fathers being less likely to express concerns about their performance as ''good fathers.'' Advocacy work was almost always taken up by mothers, not fathers, and mothers were significantly more likely than fathers to leave work or cut back on work. Gender differences in ''mothers and the micro-politics of trouble''-the dynamic whereby mothers often had to persuade their partners that something was wrong-and the perceived effect this dynamic had on the marital relationship is also highlighted.Readers interested in families and disabilities will find this book a well-written contribution to the literature on this subject, a good introduction to the subject for undergraduate readers, and a useful source of ideas for further research.

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