Article in Addiction biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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.
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
8 authors.
Robert J KohlerDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0001-7450-2490
Yasmin ZakiniaeizDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.
Terril L VerplaetseDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0002-2531-6656
C Leonardo Jimenez ChavezDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.
MacKenzie R PeltierDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.
Hang ZhouDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0002-7694-6391
Sherry A McKeeDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.
Walter RobertsDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0001-6817-884X
Funding
Yale-SCORE Resource Support CoreU54AA027989 · NIAAA · YALE UNIVERSITY · PI Ralitza Gueorguieva · 2020 to 2026
$13.0M
Characterizing brain-periphery cortisol dysregulation in AUDR01AA030971 · NIAAA · YALE UNIVERSITY · PI Terril L Verplaetse · 2024 to 2026
$2.1M
Characterizing initial recovery from alcohol use disorder and predicting heavy drinking using mobile biosensorsR01AA031959 · NIAAA · YALE UNIVERSITY · PI Sherry Ann McKee, Walter McRainey Roberts · 2024 to 2026
$2.1M
Behavioral and Neurochemical Mechanisms Underlying Stress-Precipitated DrinkingK01AA025670 · NIAAA · YALE UNIVERSITY · PI VERPLAETSE, TERRIL L · 2018 to 2023
$882k
Sex Differences in Alcohol Use Disorder Neurodegeneration using Multimodal PET and DTI NeuroimagingK01AA029706 · NIAAA · YALE UNIVERSITY · PI Yasmin Zakiniaeiz · 2022 to 2026
$881k
Developing a novel human laboratory paradigm for AUD medication screening: Modeling the ability to resist drinking and heavy drinkingR34AA031678 · NIAAA · YALE UNIVERSITY · PI MCKEE, SHERRY ANN · 2024 to 2025
$733k
Does endotoxin administration increase alcohol consumption in individuals with AUD?R03AA028361 · NIAAA · YALE UNIVERSITY · PI VERPLAETSE, TERRIL L · 2021 to 2022
In clinical settings targeting alcohol use disorder (AUD), it is often unclear whether a treatment option may best suit a patient's clinical needs. Clinicians providing AUD treatment are often required to predict patients' responses to guide treatment decisions. Recently, machine learning approaches have been used as tools in precision medicine to help guide these clinical decisions. However, the extent of their clinical utility in populations undergoing treatment is largely unknown. Using data from four Phase 2 randomized clinical trials affiliated with the NIAAA Clinical Investigations Group and a Phase 3 trial sponsored by the NIAAA, we developed a machine learning model to predict treatment response phenotypes derived from clustering drinking rates at the end of treatment. Harmonized data included demographics and baseline data from biological and clinical assessments. Follow-up analyses were performed to characterize treatment response phenotypes. Three clusters corresponding to mild (M
Indexed as
AlcoholismMachine LearningAdultAlcohol DrinkingDatasets as TopicFemaleHumansMaleMiddle AgedPrecision MedicineRandomized Controlled Trials as TopicTreatment Outcomealcohol use disordermachine learningrandomized controlled trialtreatment
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.
Empirical Derivation and Prediction of Treatment Trajectories in Harmonized AUD Clinical Trial Datasets. · full record | OpenQuestion