← Back to cohort
Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects
Scott Mackey, Nicholas Allgaier, Bader Chaarani, Philip A. Spechler, Catherine Orr, Janice Y. Bunn, Nicholas B. Allen, Nelly Alia‐Klein, Albert Batalla, Sara K. Blaine, Samantha J. Brooks, Elisabeth C. Caparelli, Yann Chye, Janna Cousijn, Alain Dagher, Sylvane Desrivières, Sarah Feldstein‐Ewing, John J. Foxe, Rita Z. Goldstein, Anna E. Goudriaan, Mary M. Heitzeg, Robert Hester, Kent E. Hutchison, Ozlem Korucuoglu, Chiang‐Shan R. Li, Edythe D. London, Valentina Lorenzetti, Maartje Luijten, Rocío Martín‐Santos, April C. May, Reza Momenan, Angelica M. Morales, Martin P. Paulus, Godfrey D. Pearlson, Marc-Étienne Rousseau, Betty Jo Salmeron, Renée S. Schluter, Lianne Schmaal, Günter Schumann, Zsuzsika Sjoerds, Dan J. Stein, Elliot A. Stein, Rajita Sinha, Nadia Solowij, Susan F. Tapert, Anne Uhlmann, Dick J. Veltman, Ruth J. van Holst, Sarah Whittle, Reínout W. Wiers, Margaret J. Wright, Murat Yücel, Sheng Zhang, Deborah Yurgelun‐Todd, Derrek P. Hibar, Neda Jahanshad, Alan C. Evans, Paul M. Thompson, David C. Glahn, Patricia Conrod, Hugh Garavan
Bibliographic record
VenueAmerican Journal of Psychiatry · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersDavid Geffen School of Medicine, University of California, Los AngelesNational Center for Complementary and Integrative HealthNational Center for Research ResourcesNational Institute of Biomedical Imaging and BioengineeringNational Institute on Drug AbuseNational Institute on Alcohol Abuse and AlcoholismMelbourne School of Psychological SciencesNational Health and Medical Research CouncilLaureate Institute for Brain Research, University of TulsaIllawarra Health and Medical Research InstituteMax-Planck-Institut für Kognitions- und NeurowissenschaftenUniversity of Cape TownJanssen Research and DevelopmentRadboud Universitair Medisch CentrumRadboud UniversiteitUniversitat de BarcelonaVrije Universiteit AmsterdamNational Institute of Mental HealthUniversity of Colorado BoulderQIMR Berghofer Medical Research InstituteUniversiteit UtrechtZonMwSchool of MedicineUniversity of VermontUniversiteit van AmsterdamMonash UniversityMedical Research CouncilUniversity of WollongongNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of Southern CaliforniaAustralian Catholic UniversityUniversiteit LeidenUniversity of OregonUniversity of California, San DiegoYale UniversityKing's College LondonVeterans Affairs San Diego Healthcare SystemMcGill UniversityUniversity of RochesterNational Institutes of HealthSan Diego State University
KeywordsBrain sizeCannabisSubstance dependenceOrbitofrontal cortexAlcohol dependenceNeuroimagingInsulaCocaine dependenceVoxel-based morphometrySubstance abuseBrain morphometryMedicinePsychologyAddictionMagnetic resonance imagingPsychiatryNeuroscienceAlcoholPrefrontal cortexCognitionWhite matterChemistryRadiology
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
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.
metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
Opus teacher head0.011
GPT teacher head0.271
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical
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".
Quick stats
Citations272
Published2018
Admission routes1
Has abstractyes