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Record W4286210023 · doi:10.1038/s41380-022-01696-3

Correction: Obesity and brain structure in schizophrenia – ENIGMA study in 3021 individuals

2022· erratum· en· W4286210023 on OpenAlexaff
Sean R. McWhinney, Katharina Brosch, Vince D. Calhoun, Benedicto Crespo‐Facorro, Nicolás Crossley, Udo Dannlowski, Erin W. Dickie, Lorielle M. F. Dietze, Gary Donohoe, Stefan S. du Plessis, Stefan Ehrlich, Robin Emsley, Petra Fürstová, David C. Glahn, Alfonso Gonzalez- Valderrama, Dominik Grotegerd, Laurena Holleran, Tilo Kircher, Pavel Knytl, Marián Kolenič, Rebekka Lencer, Igor Nenadić, Nils Opel, Julia‐Katharina Pfarr, Amanda Rodrigue, Kelly Rootes-Murdy, Alex J. Ross, Kang Sim, Antonín Škoch, Filip Španiel, Frederike Stein, Patrik Švancer, Diana Tordesillas‐Gutiérrez, Juan Undurraga, Javier Vázquez-Bourgón, Aristotle N. Voineskos, Esther Walton, Thomas W. Weickert, Cynthia Shannon Weickert, Paul M. Thompson, Theo G.M. van Erp, Jessica A. Turner, Tomáš Hájek

Bibliographic record

VenueMolecular Psychiatry · 2022
Typeerratum
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthDalhousie University
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)PsychologyObesityNeurosciencePsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

The article “Obesity and brain structure in schizophrenia – ENIGMA study in 3021 individuals”, written by Sean R. McWhinney, Katharina Brosch, Vince D. Calhoun, Benedicto Crespo-Facorro, Nicolas A. Crossley, Udo Dannlowski, Erin Dickie, Lorielle M. F. Dietze, Gary Donohoe, Stefan Plessis, Stefan Ehrlich, Robin Emsley, Petra Furstova, David C. Glahn, Alfonso Gonzalez- Valderrama, Dominik Grotegerd, Laurena Holleran, Tilo T. J. Kircher, Pavel Knytl, Marian Kolenic, Rebekka Lencer, Igor Nenadić, Nils Opel, Julia-Katharina Pfarr, Amanda L. Rodrigue, Kelly Rootes-Murdy, Alex J. Ross, Kang Sim, Antonín Škoch, Filip Spaniel, Frederike Stein, Patrik Švancer, Diana Tordesillas-Gutiérrez, Juan Undurraga, Javier Váquez-Bourgon, Aristotle Voineskos, Esther Walton, Thomas W. Weickert, Cynthia Shannon Weickert, Paul M. Thompson, Theo G. M. Erp, Jessica A. Turner, Tomas Hajek, was originally published electronically on the publisher’s internet portal on 14 June 2022 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 20 May 2022 to © The Author(s) 2022 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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)0.005
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.063
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.003
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0040.002
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0650.026

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 head0.006
GPT teacher head0.260
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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 designObservational
Domainnot available
GenreOther

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

Citations3
Published2022
Admission routes1
Has abstractno

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