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A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement
Hamed Ekhtiari, Peyman Ghobadi‐Azbari, Axel Thielscher, Andrea Antal, Lucia M. Li, A. Duke Shereen, Yuranny Cabral‐Calderín, Daniel Keeser, Til Ole Bergmann, Asif Jamil, Inês R. Violante, Jorge Almeida, Marcus Meinzer, Hartwig R. Siebner, Adam J. Woods, Charlotte J. Stagg, Rany Abend, Daria Antonenko, Tibor Auer, Marc Bächinger, Chris Baeken, Helen C. Barron, Henry W. Chase, Jenny Crinion, Abhishek Datta, Matthew H. Davis, Mohsen Ebrahimi, Zeinab Esmaeilpour, Brian Falcone, Valentina Fiori, Iman Ghodratitoostani, Gadi Gilam, Roland H. Grabner, Joel D. Greenspan, Georg Groen, Gesa Hartwigsen, Tobias U. Hauser, Christoph S. Herrmann, Chi‐Hung Juan, Bart Krekelberg, Stéphanie Lefebvre, Sook‐Lei Liew, Kristoffer H. Madsen, Rasoul Khayati, Nastaran Malmir, Paola Marangolo, Andrew Martin, Timothy J. Meeker, Hossein Mohaddes Ardabili, Marius Moisa, Davide Momi, Beni Mulyana, Alexander Opitz, Natasza Orlov, Patrick Ragert, Christian C. Ruff, Giulio Ruffini, Michaela Ruttorf, Arshiya Sangchooli, Klaus Schellhorn, Gottfried Schlaug, Bernhard Sehm, Ghazaleh Soleimani, Hosna Tavakoli, Benjamin Thompson, Dagmar Timmann, Aki Tsuchiyagaito, Ulrich Martin, Johannes Vosskuhl, Christiane Anne Weinrich, Mehran Zare-Bidoky, Xiaochu Zhang, Benedikt Zoefel, Michael A. Nitsche, Marom Bikson
Bibliographic record
VenueNature Protocols · 2022
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of WaterlooCentre for Addiction and Mental Health
FundersNational Institute on Drug AbuseNational Institute of Mental HealthNeuroscience Center Zurich, University of ZurichCentro de Ciências Matemáticas Aplicadas à IndústriaFundação para a Ciência e a TecnologiaLeibniz-GemeinschaftBiotechnology and Biological Sciences Research CouncilQueen Elisabeth Medical FoundationNational Institutes of HealthFondation Médicale Reine ElisabethUniversity of SurreyMinistry of Science and Technology, TaiwanVrije Universiteit BrusselUniversity College LondonLundbeckfondenLaureate Institute for Brain Research, University of TulsaTechnische Universiteit EindhovenMinistero della SaluteCanadian Institutes of Health ResearchFonds Wetenschappelijk OnderzoekEidgenössische Technische Hochschule ZürichBoehringer Ingelheim StiftungLudwig-Maximilians-Universität MünchenVlaamse regeringNational Institute for Health and Care ResearchDeutsche ForschungsgemeinschaftGentofte HospitalEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentChongqing Science and Technology CommissionJohns Hopkins UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEberhard Karls Universität TübingenUniversidade de CoimbraJacobs FoundationUniversidade de São PauloEuropean CommissionFundação de Amparo à Pesquisa do Estado de São PauloDeutscher Akademischer AustauschdienstNiedersächsisches Ministerium für Wissenschaft und KulturOklahoma Center for the Advancement of Science and TechnologyUniversity of OxfordNational Institute of Neurological Disorders and StrokeUniversität ZürichNatural Sciences and Engineering Research Council of CanadaBundesministerium für Bildung und ForschungUniversity of QueenslandDivision of Mathematical SciencesWellcome TrustLeibniz-Institut für Arbeitsforschung an der TU DortmundH. Lundbeck A/SMedical Research CouncilMcKnight FoundationNational Institute of General Medical SciencesNational Alliance for Research on Schizophrenia and DepressionUniversity of PittsburghBispebjerg HospitalNational Science FoundationFaculty of Health and Medical Sciences, University of Western AustraliaMax-Planck-GesellschaftRoyal Society
KeywordsChecklistPsychologyDelphi methodDelphiMedicineComputer scienceArtificial intelligenceCognitive psychology
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 head0.677
GPT teacher head0.629
Teacher spread0.048 · 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 teacher head, not a consensus.
Study designNot applicable
Domainnot available
GenreReview
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
Citations56
Published2022
Admission routes2
Has abstractno