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Record W4200001458 · doi:10.1016/j.jaac.2021.11.030

Brain Signatures During Reward Anticipation Predict Persistent Attention-Deficit/Hyperactivity Disorder Symptoms

2021· article· en· W4200001458 on OpenAlexaff
Di Chen, Tianye Jia, Wei Cheng, Miao Cao, Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Uli Bromberg, Christian Büchel, Sylvane Desrivières, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Bernd Ittermann, Jean‐Luc Martinot, Marie‐Laure Paillère Martinot, Frauke Nees, Dimitri Papadopoulos Orfanos, Tomáš Paus, Luise Poustka, Juliane H. Fröhner, Michael N. Smolka, Henrik Walter, Robert Whelan, Trevor W. Robbins, Barbara J. Sahakian, Günter Schumann, Jianfeng Feng

Bibliographic record

VenueJournal of the American Academy of Child & Adolescent Psychiatry · 2021
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNational Institute of Biomedical Imaging and BioengineeringMedical Research CouncilNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectUniversité Paris-SudFédération pour la Recherche sur le CerveauFondation pour la Recherche MédicaleNational Institute of Mental HealthScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of ChinaInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la RechercheNational Institute on Drug AbuseScience Foundation IrelandEuropean CommissionDeutsche ForschungsgemeinschaftKing's College LondonFondation de FranceBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchSouth London and Maudsley NHS Foundation TrustKing’s College LondonNational Institutes of HealthFondation de l'Avenir pour la Recherche Médicale Appliquée
KeywordsAnticipation (artificial intelligence)Attention deficit hyperactivity disorderPsychologyAttention deficitNeuroscienceCognitive psychologyClinical psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex
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 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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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
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

Citations16
Published2021
Admission routes1
Has abstractno

Explore more

Same venueJournal of the American Academy of Child & Adolescent PsychiatrySame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207