Correction: Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research
Notice bibliographique
Résumé
Author notes These authors contributed equally: Na Cai, Brad Verhulst. Authors and Affiliations Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany Na Cai Computational Health Centre, Helmholtz Munich, Neuherberg, Germany Na Cai School of Medicine and Health, Technical University of Munich, Munich, Germany Na Cai Department of Psychiatry and Behavioral Sciences, Texas A&M University, College Station, TX, USA Brad Verhulst Centre of Precision Psychiatry, University of Oslo, Oslo, Norway Ole A. Andreassen Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway Ole A. Andreassen KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway Ole A. Andreassen Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands Jan Buitelaar Karakter Child and Adolescent University Center, Nijmegen, The Netherlands Jan Buitelaar Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA Howard J. Edenberg Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA Howard J. Edenberg & John I. Nurnberger Jr Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA John M. Hettema, Michael C. Neale & Kenneth S. Kendler Departments of Psychiatry and Genetics, University of Pennsylvania, Philadelphia, PA, USA Michael Gandal Lifespan Brain Institute at Penn Med and the Children’s Hospital of Philadelphia, Philadelphia, PA, USA Michael Gandal Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA Andrew Grotzinger Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA Andrew Grotzinger Department of Psychiatry & Behavioral Health, Stony Brook University, Stony Brook, NY, USA Katherine Jonas Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA Phil Lee Department of Psychiatry, Harvard Medical School, Boston, MA, USA Phil Lee Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Travis T. Mallard & Jordan W. Smoller Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA Travis T. Mallard & Jordan W. Smoller Department of Community Health and Epidemiology and Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada Manuel Mattheisen Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital of Munich, Munich, Germany Manuel Mattheisen Department of Biomedicine, Aarhus University, Aarhus, Denmark Manuel Mattheisen Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA Michael C. Neale & Kenneth S. Kendler Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA John I. Nurnberger Jr Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA John I. Nurnberger Jr Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands Wouter J. Peyrot Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands Wouter J. Peyrot Department of Psychology, University of Texas at Austin, Austin, TX, USA Elliot M. Tucker-Drob Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA Jordan W. Smoller Authors Na Cai View author publications You can also search for this author in PubMed Google Scholar Brad Verhulst View author publications You can also search for this author in PubMed Google Scholar Ole A. Andreassen View author publications You can also search for this author in PubMed Google Scholar Jan Buitelaar View author publications You can also search for this author in PubMed Google Scholar Howard J. Edenberg View author publications You can also search for this author in PubMed Google Scholar John M. Hettema View author publications You can also search for this author in PubMed Google Scholar Michael Gandal View author publications You can also search for this author in PubMed Google Scholar Andrew Grotzinger View author publications You can also search for this author in PubMed Google Scholar Katherine Jonas View author publications You can also search for this author in PubMed Google Scholar Phil Lee View author publications You can also search for this author in PubMed Google Scholar Travis T. Mallard View author publications You can also search for this author in PubMed Google Scholar Manuel Mattheisen View author publications You can also search for this author in PubMed Google Scholar Michael C. Neale View author publications You can also search for this author in PubMed Google Scholar John I. Nurnberger Jr View author publications You can also search for this author in PubMed Google Scholar Wouter J. Peyrot View author publications You can also search for this author in PubMed Google Scholar Elliot M. Tucker-Drob View author publications You can also search for this author in PubMed Google Scholar Jordan W. Smoller View author publications You can also search for this author in PubMed Google Scholar Kenneth S. Kendler View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to Kenneth S. Kendler .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,227 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,006 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,121 | 0,039 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».