Trial reportSubstance use & addiction journal2024
Prediction Rules Identify Which Young Adults Have Higher Rates of Heavy Episodic Drinking After Exposure to 12-Week Text Message Interventions.
Trial report in Substance use & addiction journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed, 4 citations in OpenAlex.
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundAn alcohol text message intervention recently demonstrated effects in reducing heavy episodic drinking (HED) days at the three month follow-up in young adults with a history of hazardous drinking. An important next step in understanding intervention effects involves identifying baseline participant characteristics that predict who will benefit from intervention exposure to support clinical decision-making and guide further intervention development. To identify baseline characteristics that predict HED, this exploratory study used a prediction rule ensemble (PRE). Compared to more complex decision-tree methods (e.g., random forest), PREs have comparable performance, while generating simpler rules that can directly identify subgroups that do or do not respond to intervention.
methodsThis secondary analysis examined data from 916 young adults who reported HED (68.5% female, mean age = 22.1, SD = 2.1), were enrolled in an alcohol text message randomized clinical trial and who completed baseline assessment and the three month follow-up. A PRE with ten fold cross-validation, which included 21 baseline variables representing sociodemographic characteristics (e.g., sex, age, race, ethnicity, college enrollment), alcohol consumption (frequency of alcohol consumption, quantity consumed on a typical drinking day, frequency of HED), impulsivity subscales (i.e., negative urgency, positive urgency, lack of premeditation, lack of perseverance, sensation seeking), readiness to change, perceived peer drinking and HED-related consequences, and intervention status were used to predict HED at the three month follow-up.
resultsThe PRE identified 12 rules that predicted HED at three months (
conclusionsThe rules provide interpretable decision-making tools that predict who has higher alcohol consumption following exposure to alcohol text message interventions using baseline participant characteristics (prior to intervention), which highlight the importance of interventions related to negative urgency and peer alcohol use.
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What OpenQuestion holds
Registered trials
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.