A Social Media-Based Acute Alcohol Consumption Behavior (NekNomination): Case Series in Italian Emergency Departments
Bibliographic record
Abstract
BACKGROUND: NekNomination, also known as NekNominate, Neck and Nominate, or Neck Nomination, is a social network-based drinking game which is thought to have originated in Australia and spread all over the world between 2013 and 2014. Individuals record videos of themselves while rapidly drinking excessive quantities of alcoholic drinks (necking) and then nominate friends to outdo them within 24 hours; the videos are then posted on social media such as Facebook or YouTube. The consequences of this drinking game have been very dangerous; at least 5 people under age 30 years have died after drinking deadly cocktails, and many others have suffered from alcohol intoxication. OBJECTIVE: The goal of the research is to evaluate data about clinically important acute alcohol intoxication among teenagers and young adults and inform and educate the general public, especially parents, teachers, and health workers, about the spreading craze of dangerous Internet-related behavior among today's teenagers and young people up to the age of 23 years. METHODS: Patients aged 15 to 23 years with acute alcohol intoxication who came to the emergency department (ED) of 2 major hospitals in Italy from January 1, 2011, to June 30, 2014, were included in this study. Data were retrieved from prehospital and intrahospital medical records and included personal information, methods of intoxication, triage color code, date and time of access to the ED, any relevant signs and symptoms, blood alcohol concentration, and diagnosis at discharge. RESULTS: A total of 450 young patients (male 277/450, 61.5%, female 173/450, 38.5%; age 15 to 16 years 15/450, 3.3%, age 17 to 18 years 184/450, 40.9%, age 19 to 23 years 251/450, 55.8%) were recruited. The causes of intoxication were happy hour, binge drinking, NekNominate, eyeballing, other alcoholic games, or a mix of them. Happy hour was found to be more common among the older patients, whereas NekNominate accounted for almost half of the youngest group of hospitalizations. Eyeballing occurred in 1.6% (7/450) of cases; binge drinking and other alcoholic games caused 23.3% (105/450) and 23.8% (107/450) of hospitalizations, respectively. On admission, 44.2% (199/450) of patients were assigned a red or yellow color code requiring immediate medical attention; about 14% of them required additional medical assistance (after being in the ED) or hospitalization, some in semi-intensive care units. CONCLUSIONS: Our study shows that the increased numbers of hospitalizations due to alcohol intoxication in the adolescent age group, as a consequence of NekNominate or other drinking games, is alarming and represents a serious public health issue. The potential markers of improper use of social networks must be clearly identified, including categories at risk of alcohol abuse, in order to develop intervention and prevention strategies in terms of education and awareness, which may help in averting potentially fatal episodes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".