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Record W4293105499 · doi:10.1017/s0033291722002446

Characterizing eating behavioral phenotypes in mood disorders: a narrative review

2022· review· en· W4293105499 on OpenAlexaff
Elena Koning, Jacob Vorstman, Roger S. McIntyre, Elisa Brietzke

Bibliographic record

VenuePsychological Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsToronto Western HospitalHospital for Sick ChildrenUniversity of TorontoUniversity Health NetworkQueen's University
Fundersnot available
KeywordsEating disordersMoodPsychologyNarrative reviewClinical psychologyNarrativeMood disordersPhenotypePsychiatryPsychotherapistAnxietyGeneticsBiologyArt

Abstract

fetched live from OpenAlex

Mood disorders, including depressive and bipolar disorders, represent a multidimensional and prevalent group of psychiatric illnesses characterized by disturbances in emotion, cognition and metabolism. Maladaptive eating behaviors in mood disorders are diverse and warrant characterization in order to increase the precision of diagnostic criteria, identify subtypes and improve treatment strategies. The current narrative review synthesizes evidence for Eating Behavioral Phenotypes (EBP) in mood disorders as well as advancements in pathophysiological conceptual frameworks relevant to each phenotype. Phenotypes include maladaptive eating behaviors related to appetite, emotion, reward, impulsivity, diet style and circadian rhythm disruption. Potential treatment strategies for each phenotype are also discussed, including psychotherapeutic, pharmacological and nutritional interventions. Maladaptive eating behaviors related to mood disorders are relevant from both clinical and research perspectives, yet have been somewhat overlooked thus far. A better understanding of this aspect of mood disorders holds promise to improve clinical care in this patient group and contribute to the subtyping of these currently subjectively diagnosed and treated disorders.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.182
GPT teacher head0.500
Teacher spread0.317 · 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.

Study designOther design
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

Citations27
Published2022
Admission routes1
Has abstractyes

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