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Development of Disordered Eating Behaviors and Comorbid Depressive Symptoms in Adolescence: Neural and Psychopathological Predictors

2020· article· en· W3034253694 on OpenAlexaff
Zuo Zhang, Lauren Robinson, Tianye Jia, Erin Burke Quinlan, Nicole Tay, Congying Chu, Edward D. Barker, Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Bernd Ittermann, Jean‐Luc Martinot, Argyris Stringaris, Jani Penttilä, Betteke Maria van Noort, Yvonne Grimmer, Marie‐Laure Paillère Martinot, Corinna Isensee, Andreas Becker, Frauke Nees, Dimitri Papadopoulos Orfanos, Tomáš Paus, Luise Poustka, Sarah Hohmann, Juliane H. Fröhner, Michael N. Smolka, Henrik Walter, Robert Whelan, Günter Schumann, Ulrike Schmidt, Sylvane Desrivières

Bibliographic record

VenueBiological Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
FundersHorizon 2020Sixth Framework ProgrammeSeventh Framework ProgrammeUniversité Paris-SudShireUniversité Paris-SaclayFondation pour la Recherche MédicaleEU Joint Programme – Neurodegenerative Disease ResearchInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la RechercheMedical Research CouncilUniversité Paris DescartesMedical Research FoundationUniversität HeidelbergEuropean CommissionSouth London and Maudsley NHS Foundation TrustNIHR Maudsley Biomedical Research CentreDeutsche ForschungsgemeinschaftKing's College LondonFondation de FranceBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchKing’s College LondonNational Institutes of HealthFondation de l'Avenir pour la Recherche Médicale Appliquée
KeywordsPsychopathologyEating disordersPsychologyBinge eatingAttention deficit hyperactivity disorderClinical psychologyPsychiatryMajor depressive disorderDisordered eatingMood

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 distilled prediction

Teacher imitation

Not 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.303
Teacher spread0.271 · 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 teacher head, 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

Citations42
Published2020
Admission routes1
Has abstractno

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