Survival of Very High Blood Alcohol Concentration Without Consequential Damage in a Patient Without a Previous Substance Use Disorder

Intoxications with alcohol may lead to death depending on (maximum) blood alcohol concentration (BAC) and accompanying factors such as liver function, tolerance, and comedication. Death may occur due to ethanol‐induced respiratory depression and/or aspiration of gastric content (due to an impaired gag reflex); thus, securing of the airway and ventilation is occasionally necessary. A case of a 58‐year‐old female patient with depression who demonstrated a very high BAC of 8.68 gm/L (0.868%) following ingestion of large amounts of alcohol with suicidal intent is presented. Intubation and ventilation were life‐saving, and the patient did not develop any physical or consequential damage. As the patient had not regularly used alcohol or any other psychotropic agent, tolerance could be ruled out. This case emphasizes the necessity of rapid securing of the airway in patients with alcohol intoxication and respiratory depression and, furthermore, illustrates the large interindividual differences regarding ethanol susceptibility.     

The Thucydides Trap and the Korean Peninsula: So why Won’t the USA and China Get Caught?

Journal of Chinese Political Science - “It was the rise of Athens and the fear that it instilled in Sparta that made war inevitable” as Thucydides famously stated. But are we destined...     

Paving the path: drivers of standardization participation at ISO

The Journal of Technology Transfer - Several studies investigating the trade effects of standardization have found a positive impact of the number of international standards in a country on its...     

Using Named Entities for Computer‐Automated Verbal Deception Detection

There is an increasing demand for automated verbal deception detection systems. We propose named entity recognition (NER; i.e., the automatic identification and extraction of information from text) to model three established theoretical principles: (i) truth tellers provide accounts that are richer in detail, (ii) contain more contextual references (specific persons, locations, and times), and (iii) deceivers tend to withhold potentially checkable information. We test whether NER captures these theoretical concepts and can automatically identify truthful versus deceptive hotel reviews. We extracted the proportion of named entities with two NER tools (spaCy and Stanford's NER) and compared the discriminative ability to a lexicon word count approach (LIWC) and a measure of sentence specificity (speciteller). Named entities discriminated truthful from deceptive hotel reviews above chance level, and outperformed the lexicon approach and sentence specificity. This investigation suggests that named entities may be a useful addition to existing automated verbal deception detection approaches.     

Power in the Age of Datafication: Exploring China’s Global Data Power

Journal of Chinese Political Science - Anxieties about China’s growing data power have begun to drive geopolitical and technological competition. Yet, the size of Chinese data power is...