Article in Addiction biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing 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
6 authors.
Magdalini PaschaliDepartment of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.ORCID 0000-0003-4967-3137
Qingyu ZhaoDepartment of Radiology, Weill Cornell Medicine, New York, NY, USA.
Stephanie A SassoonDepartment of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.
Adolf PfefferbaumDepartment of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.
Edith V SullivanDepartment of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.ORCID 0000-0001-6739-3716
Kilian M PohlDepartment of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.
Funding
TRACKING HIV INFECTION AND ALCOHOL ABUSE CNS COMORBIDITY WITH NEUROIMAGINGU01AA017347 · NIAAA · SRI INTERNATIONAL · PI PFEFFERBAUM, ADOLF, SULLIVAN, EDITH VIONI · 2007 to 2021
$16.0M
CNS DEFICITS: INTERACTION OF AGE AND ALCOHOLISMR01AA005965 · NIAAA · STANFORD UNIVERSITY · PI PFEFFERBAUM, ADOLF, ZAHR, NATALIE M · 1985 to 2025
$13.0M
DATA COORDINATING CENTER TO PROVIDE SERVICES TO WORK COLLABORATIVELY WITH THE NATIONAL NEUROHIV TISSUE CONSORTIUM CLINICAL SITES TO PROVIDE MANAGEMENT AND DATABASE CAPABILITIES, SCIENTIFIC EXPERTISE, 75N95023C00013 · NIDA · THE EMMES COMPANY, LLC · PI SHERMAN, SETH · 2023 to 2025
$11.2M
CEREBELLAR STRUCTURE AND FUNCTION IN ALCOHOLISMR01AA010723 · NIAAA · STANFORD UNIVERSITY · PI SULLIVAN, EDITH VIONI, ZAHR, NATALIE M · 1996 to 2025
$8.3M
Machine learning to distinguish HAND from Alzheimer's disease in HIV over age 60R01MH113406 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI POHL, KILIAN MARIA, VALCOUR, VICTOR · 2017 to 2021
$5.5M
Cerebellar Structure and Function in AlcoholismR37AA010723 · NIAAA · STANFORD UNIVERSITY · PI SULLIVAN, EDITH VIONI · 2010 to 2019
$5.4M
Tracking HIV Infection & Alcohol Abuse CNS Comorbidity with NeuroimagingR01AA017347 · NIAAA · SRI INTERNATIONAL · PI Adolf Pfefferbaum · 2022 to 2026
$5.2M
CNS DEFICITS: INTERACTION OF AGE AND ALCOHOLISMR37AA005965 · NIAAA · STANFORD UNIVERSITY · PI PFEFFERBAUM, ADOLF · 1996 to 2005
$4.4M
Interpretable Deep Forecasting of Hazardous Substance Use during High SchoolR01DA057567 · NIDA · STANFORD UNIVERSITY · PI Kilian Maria Pohl, Susan F. Tapert · 2022 to 2026
$2.3M
Longitudinal Analysis of Diffusion Tensor Imaging to Discover Adolescent Alcohol Use EffectR00AA028840 · NIAAA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Qingyu Zhao · 2024 to 2026
$747k
Longitudinal Analysis of Diffusion Tensor Imaging to Discover Adolescent Alcohol Use EffectK99AA028840 · NIAAA · STANFORD UNIVERSITY · PI ZHAO, QINGYU · 2021 to 2022
Grip strength is considered one of the simplest and reliable indices of general health. Although motor ability and strength are commonly affected in people with alcohol use disorder (AUD), factors predictive of grip strength decline in AUD have not been investigated. Here, we employed a data-driven analysis predicting grip strength from measurements in 53 controls and 110 AUD participants, 53 of whom were comorbid with HIV infection. Controls and AUD were matched on sex, age, and body mass index. Measurements included commonly available metrics of brain structure, neuropsychological functioning, behavioural status, haematological and health status, and demographics. Based on 5-fold stratified cross-validation, a machine learning approach predicted grip strength separately for each cohort. The strongest (top 10%) predictors of grip were then tested against grip strength with correlational analysis. Leading grip strength predictors for both cohorts were cerebellar volume and mean corpuscular haemoglobin concentration. Predictors specific to controls were Backwards Digit Span, precentral gyrus volume, diastolic blood pressure, and mean platelet volume, which together significantly predicted grip strength (R
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.
Multi-domain predictors of grip strength differentiate individuals with and without alcohol use disorder. · full record | OpenQuestion