Evidence map›Paper›PMID 34744927›Full record

ArticleFrontiers in psychology2021

Characteristics Associated With High-Intensity Binge Drinking in Alcohol Use Disorder.

Joshua L Gowin, Matthew E Sloan, James K Morris, Melanie L Schwandt, Nancy Diazgranados, Vijay A Ramchandani

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey.Proceedings of the 2024 9th International Conference on Mathematics and Artificial Intelligence · 2024
    Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Joshua L GowinDepartment of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Matthew E SloanAddictions Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.
James K MorrisLaboratory on Human Psychopharmacology, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States.
Melanie L SchwandtDepartment of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada.
Nancy DiazgranadosDepartment of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada.
Vijay A RamchandaniLaboratory on Human Psychopharmacology, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States.
National Institute on Alcohol Abuse and Alcoholism · USUniversity of Toronto · CACentre for Addiction and Mental Health · CAUniversity of Colorado Anschutz Medical Campus · US

Funding

PREVENTION OF DUI RECIDIVISM AFTER TRAUMAK23AA000310 · NIAAA · UNIVERSITY OF NEW MEXICO · PI SCHERMER, CAROL R · 2000 to 2004
$695k
NIAAA NIH HHS K23 AA000310
6 · The paper itself

Abstract

High-intensity binge drinking, defined as consuming 2-3 times the level of a binge (4 or 5 drinks for women or men), increases the risks of overdose and alcohol-related cancer relative to lower levels of drinking. This study examined the relationship between high-intensity binge drinking and three domains hypothesized to contribute to alcohol use disorder (AUD): incentive salience, negative emotionality, and executive function. This cross-sectional study at the National Institute on Alcohol Abuse and Alcoholism examined 429 adults with AUD and 413 adults without a history of AUD. Drinking was assessed using the 90-day Timeline Followback interview. The AUD sample was divided into training and testing sets, and a machine learning model was generated in the training set and then applied to the testing set, to classify individuals based on if they had engaged in high-intensity binge drinking. We also conducted regression models for the following dependent variables: the presence of high-intensity binge drinking, frequency of high-intensity binge drinking, and number of drinks per of binge. Independent variables in these regression models were determined by variable selection from the machine learning algorithm and included time thinking about alcohol, depression rating, and positive urgency as representative variables for the three domains. These variables were assessed using self-report measures. The models were applied to the adults without a history of AUD to determine generalizability. The machine learning algorithm displayed reasonable accuracy when classifying individuals as high-intensity binge drinkers (area under ROC=0.74, 95% CI 0.67, 0.80). In adults with AUD, greater depression rating (OR=1.04, 95% CI 1.01, 1.070) and amount of time thinking about alcohol (OR=1.48, 95% CI 1.20, 1.91) were associated with greater likelihood of high-intensity binge drinking. They were also associated with greater frequency of high-intensity binge drinking days and greater number of drinks on binge occasions. Our findings suggest that incentive salience may contribute to high-intensity binge drinking in both controls and individuals with AUD. Negative emotionality was only associated with high-intensity binge drinking in individuals diagnosed with AUD, suggesting that it may be a consequence rather than a cause of high-intensity binge drinking.

Indexed as

alcoholbinge alcohol consumptionelastic netrandom forestssubstance use and misuse

Identifiers

PMID34744927
PMCPMC8564144
OpenAlexW3205819182

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.