Modeling alcohol use disorder severity: An integrative structural equation modeling approach

Background: Alcohol dependence is a complex psychological disorder whose phenomenology changes as the disorder progresses. Neuroscience has provided a variety of theories and evidence for the development, maintenance, and severity of addiction; however, clinically, it has been difficult to evaluate alcohol use disorder (AUD) severity.Objective: This study seeks to evaluate and validate a data-driven approach to capturing alcohol severity in a community sample.Method: Participants were non-treatment seeking problem drinkers (n = 283). A structural equation modeling approach was used to (a) verify the latent factor structure of the indices of AUD severity; and (b) test the relationship between the AUD severity factor and measures of alcohol use, affective symptoms, and motivation to change drinking.Results: The model was found to fit well, with all chosen indices of AUD severity loading significantly and positively onto the severity factor. In addition, the paths from the alcohol use, motivation, and affective factors accounted for 68% of the variance in AUD severity. Greater AUD severity was associated with greater alcohol use, increased affective symptoms, and higher motivation to change.Conclusion: Unlike the categorical diagnostic criteria, the AUD severity factor is comprised of multiple quantitative dimensions of impairment observed across the progression of the disorder. The AUD severity factor was validated by testing it in relation to other outcomes such as alcohol use, affective symptoms, and motivation for change. Clinically, this approach to AUD severity can be used to inform treatment planning and ultimately to improve outcomes. © 2013 Moallem, Courtney, Bacio and Ray

Storm severity detection (RF)

Measurement of lightning location data which occur together with continental thunderstorms and hurricanes was examined, and a second phase linear interferometer was deployed. Electrical emission originating from tropical storms in the Gulf of Mexico were monitored. The time span between hurricane ALLEN (10 August 1980) and hurricane ALICIA (18 August 1983) represents the longest period that the United States has gone without hurricane landfall. Both systems were active and data were acquired during the landfall period of hurricane ALICIA

Severity as a Priority Setting Criterion: Setting a Challenging Research Agenda

Priority setting in health care is ubiquitous and health authorities are increasingly recognising the need for priority setting guidelines to ensure efficient, fair, and equitable resource allocation. While cost-effectiveness concerns seem to dominate many policies, the tension between utilitarian and deontological concerns is salient to many, and various severity criteria appear to fill this gap. Severity, then, must be subjected to rigorous ethical and philosophical analysis. Here we first give a brief history of the path to today’s severity criteria in Norway and Sweden. The Scandinavian perspective on severity might be conducive to the international discussion, given its long-standing use as a priority setting criterion, despite having reached rather different conclusions so far. We then argue that severity can be viewed as a multidimensional concept, drawing on accounts of need, urgency, fairness, duty to save lives, and human dignity. Such concerns will often be relative to local mores, and the weighting placed on the various dimensions cannot be expected to be fixed. Thirdly, we present what we think are the most pertinent questions to answer about severity in order to facilitate decision making in the coming years of increased scarcity, and to further the understanding of underlying assumptions and values that go into these decisions. We conclude that severity is poorly understood, and that the topic needs substantial further inquiry; thus we hope this article may set a challenging and important research agenda

A proposed scoring system for assessing the severity of actinic keratosis on the head: actinic keratosis area and severity index

Background: Actinic keratosis (AK) severity is currently evaluated by subjective assessment of patients. Objectives: To develop and perform an initial pilot validation of a new easy-to-use quantitative tool for assessing AK severity on the head. Methods: The actinic keratosis area and severity index (AKASI) for the head was developed based on a review of other severity scoring systems in dermatology, in particular the psoriasis area and severity index (PASI). Initial validation was performed by 13 physicians assessing AK severity in 18 AK patients and two controls using a physician global assessment (PGA) and AKASI. To determine an AKASI score, the head was divided into four regions (scalp, forehead, left/right cheek ear, chin and nose). In each region, the percentage of the area affected by AKs was estimated, and the severities of three clinical signs of AK were assessed: distribution, erythema and thickness. Results: There was a strong correlation between AKASI and PGA scores (Pearson correlation coefficient: 0.86). AKASI was able to discriminate between different PGA categories: mean (SD) AKASI increased from 2.88 (1.18) for ‘light’ to 5.33 (1.48) for ‘moderate’, 8.28 (1.89) for ‘severe’, and 8.73 (3.03) for ‘very severe’ PGA classification. The coefficient of variation for AKASI scores was low and relatively constant across all PGA categories. Conclusions: Actinic keratosis area and severity index is proposed as a new quantitative tool for assessing AK severity on the head. It may be useful in the future evaluation of new AK treatments in clinical studies and the management of AK in daily practice

Trauma and Trichotillomania: A Tenuous Relationship

Some have argued that hair pulling in trichotillomania (TTM) is triggered by traumatic events, but reliable evidence linking trauma to TTM is limited. However, research has shown that hair pulling is associated with emotion regulation, suggesting a connection between negative affect and TTM. We investigated the associations between trauma, negative affect, and hair pulling in a cross-sectional sample of treatment seeking adults with TTM (N=85). In the current study, participants’ self-reported traumatic experiences were assessed during a structured clinical interview, and participants completed several measures of hair pulling severity, global TTM severity, depression, anxiety, experiential avoidance, and quality of life. Those who experienced trauma had more depressive symptoms, increased experiential avoidance, and greater global TTM severity. Although the presence of a trauma history was not related to the severity of hair pulling symptoms in the past week, depressive symptoms mediated the relationship between traumatic experiences and global TTM severity. These findings cast doubt on the notion that TTM is directly linked to trauma, but suggest that trauma leads to negative affect that individuals cope with through hair pulling. Implications for the conceptualization and treatment of TTM are discussed

An Attribute Selection For Severity Level Determination According To The Support Vector Machine Classification Result

Determination of bug severity level is needed in fixing bug. Actually, in bug-tracking system, there is around 14 attributes used for defining a bug. But, all this time we do not know which attributes are highly influential for this. In this research, a new model of severity type classification using Infogain method for Bugzilla is proposed. As for the classsification process, we use Support Vector Machine, because this method is suitable in handling a massive data records. In this research, 8 bug attributes and 17.746 record of bug reports are involved. From the result of the experiment, we recommend five attributes which can be used effectively in classifying the severity types with a minimal value of infogain 0,33 which is component, qa_contact, summary, cc_list and product. The combination of those 5 attributes resulting in 99,83% accuracy of severity types classification. Keywords- Bug Tracking System; Severity Level Classification; TF-IDF; Infogain; SVM