Tough Love

In this paper I examine Bernard Williams’ claim that an appealing conception of love can come into conflict with impartial morality. First, I explain how Williams’ claim can survive one strategy to head off the possibility of conflict. I then examine J.D.Velleman’s Kantian conception of love as another possible way to reject Williams’ claim. I argue, however, that Velleman’s attempt to transcend love’s partiality in his account of love produces an unappealing and unconvincing ideal. This is made particularly clear, I suggest, by the analysis that Velleman is forced to give of the kind of case that generated Williams’ observations in the first place. Thus Velleman’s account should be rejecte

Banning open carry of unloaded handguns decreases firearm-related fatalities and hospital utilization.

BackgroundSince 1967, in California it has been illegal to openly carry a loaded firearm in public except when engaged in hunting or law enforcement. However, beginning January 1, 2012, public open carry of unloaded handguns also became illegal. Fatal and non-fatal (NF) firearm injuries were examined before and after adoption of the 2012 ban to quantify the effect of the new law on public health.MethodsState-level data were obtained directly from California and nine other US state inpatient and emergency department (ED) discharge databases, and the Centers for Disease Control Web-Based Injury Statistics Query and Reporting System. Case numbers of firearm fatalities, NF hospitalizations, NF ED visits, and state-level population estimates were extracted. Each incident was classified as unintentional, self-inflicted, or assault. Crude incidence rates were calculated. The strength of gun laws was quantified using the Brady grade. There were no changes to open carry in these nine states during the study. Using a difference-in-difference technique, the rate trends 3 years preban and postban were compared.ResultsThe 2012 open carry ban resulted in a significantly lower incident rate of both firearm-related fatalities and NF hospitalizations (p<0.001). The effect of the law remained significant when controlling for baseline state gun laws (p<0.001). Firearm incident rate drops in California were significant for male homicide (p=0.023), hospitalization for NF assault (p=0.021  male; p=0.025 female), and ED NF assault visits (p=0.04). No significant decreases were observed by sex for suicides or unintentional injury. Changing the law saved an estimated 337 lives (3.6% fewer deaths) and 1285 NF visits in California during the postban period.DiscussionOpen carry ban decreases fatalities and healthcare utilization even in a state with baseline strict gun laws. The most significant impact is from decreasing firearm-related fatal and NF assaults.Level of evidenceIII, epidemiology

Trauma Early Mortality Prediction Tool (TEMPT) for assessing 28-day mortality.

Background:Prior mortality prediction models have incorporated severity of anatomic injury quantified by Abbreviated Injury Severity Score (AIS). Using a prospective cohort, a new score independent of AIS was developed using clinical and laboratory markers present on emergency department presentation to predict 28-day mortality. Methods:All patients (n=1427) enrolled in an ongoing prospective cohort study were included. Demographic, laboratory, and clinical data were recorded on admission. True random number generator technique divided the cohort into derivation (n=707) and validation groups (n=720). Using Youden indices, threshold values were selected for each potential predictor in the derivation cohort. Logistic regression was used to identify independent predictors. Significant variables were equally weighted to create a new mortality prediction score, the Trauma Early Mortality Prediction Tool (TEMPT) score. Area under the curve (AUC) was tested in the validation group. Pairwise comparison of Trauma Injury Severity Score (TRISS), Revised Trauma Score, Glasgow Coma Scale, and Injury Severity Score were tested against the TEMPT score. Results:There was no difference between baseline characteristics between derivation and validation groups. In multiple logistic regression, a model with presence of traumatic brain injury, increased age, elevated systolic blood pressure, decreased base excess, prolonged partial thromboplastin time, increased international normalized ratio (INR), and decreased temperature accurately predicted mortality at 28 days (AUC 0.93, 95% CI 0.90 to 0.96, P<0.001). In the validation cohort, this score, termed TEMPT, predicted 28-day mortality with an AUC 0.94 (95% CI 0.92 to 0.97). The TEMPT score preformed similarly to the revised TRISS score for severely injured patients and was highly predictive in those having mild to moderate injury. Discussion:TEMPT is a simple AIS-independent mortality prediction tool applicable very early following injury. TEMPT provides an AIS-independent score that could be used for early identification of those at risk of doing poorly following even minor injury. Level of evidence:Level II

Finding the signal in the noise: Could social media be utilized for early hospital notification of multiple casualty events?

IntroductionDelayed notification and lack of early information hinder timely hospital based activations in large scale multiple casualty events. We hypothesized that Twitter real-time data would produce a unique and reproducible signal within minutes of multiple casualty events and we investigated the timing of the signal compared with other hospital disaster notification mechanisms.MethodsUsing disaster specific search terms, all relevant tweets from the event to 7 days post-event were analyzed for 5 recent US based multiple casualty events (Boston Bombing [BB], SF Plane Crash [SF], Napa Earthquake [NE], Sandy Hook [SH], and Marysville Shooting [MV]). Quantitative and qualitative analysis of tweet utilization were compared across events.ResultsOver 3.8 million tweets were analyzed (SH 1.8 m, BB 1.1m, SF 430k, MV 250k, NE 205k). Peak tweets per min ranged from 209-3326. The mean followers per tweeter ranged from 3382-9992 across events. Retweets were tweeted a mean of 82-564 times per event. Tweets occurred very rapidly for all events (<2 mins) and represented 1% of the total event specific tweets in a median of 13 minutes of the first 911 calls. A 200 tweets/min threshold was reached fastest with NE (2 min), BB (7 min), and SF (18 mins). If this threshold was utilized as a signaling mechanism to place local hospitals on standby for possible large scale events, in all case studies, this signal would have preceded patient arrival. Importantly, this threshold for signaling would also have preceded traditional disaster notification mechanisms in SF, NE, and simultaneous with BB and MV.ConclusionsSocial media data has demonstrated that this mechanism is a powerful, predictable, and potentially important resource for optimizing disaster response. Further investigated is warranted to assess the utility of prospective signally thresholds for hospital based activation

Digital Twins in Civil Infrastructure Systems

This research explores the existing definitions, concepts and applications surrounding the efficient implementation and use of digital twins (DTs) within civil infrastructure systems (CISs). The CISs within the scope of this research are as follows: transportation, energy, telecommunications, water and waste, as well as Smart Cities, which encompasses all of the previous. The research methodology consists of a review of current literature, a series of semi-structured interviews and a detailed survey. The outcome of this work is a refined definition of DTs within CISs, in addition to a set of recommendations for both future academic research and industry best practice

Characterizing the gut microbiome in trauma: significant changes in microbial diversity occur early after severe injury.

Background:Recent studies have demonstrated the vital influence of commensal microbial communities on human health. The central role of the gut in the response to injury is well described; however, no prior studies have used culture-independent profiling techniques to characterize the gut microbiome after severe trauma. We hypothesized that in critically injured patients, the gut microbiome would undergo significant compositional changes in the first 72 hours after injury. Methods:Trauma stool samples were prospectively collected via digital rectal examination at the time of presentation (0 hour). Patients admitted to the intensive care unit (n=12) had additional stool samples collected at 24 hours and/or 72 hours. Uninjured patients served as controls (n=10). DNA was extracted from stool samples and 16S rRNA-targeted PCR amplification was performed; amplicons were sequenced and binned into operational taxonomic units (OTUs; 97% sequence similarity). Diversity was analyzed using principle coordinates analyses, and negative binomial regression was used to determine significantly enriched OTUs. Results:Critically injured patients had a median Injury Severity Score of 27 and suffered polytrauma. At baseline (0 hour), there were no detectable differences in gut microbial community diversity between injured and uninjured patients. Injured patients developed changes in gut microbiome composition within 72 hours, characterized by significant alterations in phylogenetic composition and taxon relative abundance. Members of the bacterial orders Bacteroidales, Fusobacteriales and Verrucomicrobiales were depleted during 72 hours, whereas Clostridiales and Enterococcus members enriched significantly. Discussion:In this initial study of the gut microbiome after trauma, we demonstrate that significant changes in phylogenetic composition and relative abundance occur in the first 72 hours after injury. This rapid change in intestinal microbiota represents a critical phenomenon that may influence outcomes after severe trauma. A better understanding of the nature of these postinjury changes may lead to the ability to intervene in otherwise pathological clinical trajectories. Level of evidence:III. Study type:Prognostic/epidemiological