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Record W3136959484 · doi:10.1111/eip.13137

Obesity and overweight among children and adolescents with bipolar disorder from the general population: A review of the scientific literature and a meta‐analysis

2021· review· en· W3136959484 on OpenAlexfundno aff
B. Girela Serrano, Margarita Guerrero‐Jiménez, Alexander Spiers, Luís Gutiérrez-Rojas

Bibliographic record

VenueEarly Intervention in Psychiatry · 2021
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
FundersMedical Research Council CanadaFundación Alicia Koplowitz
KeywordsOverweightBipolar disorderObesityPsychiatryMedicineMeta-analysisPopulationClinical psychologyPsychologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

There is substantial evidence of the high prevalence of obesity (OB) and overweight (OW) and their association with increased medical and psychiatric burden among adults with bipolar disorder (BD). However, little is known regarding its prevalence among young people with BD, other than the risk from psychotropic medication, which has been the focus of research in this population. We present a systematic review and meta-analysis of the literature on prevalence and correlates of OB and OW children and adolescents with BD using a different perspective than impact of medication. Four studies met inclusion criteria. The prevalence of OB in children and adolescents with BD was 15% (95% CI 11-20%). We observed a higher prevalence of OB in comparison to the general population. Different studies found significant associations between OB, OW, and BD in young populations including non-Caucasian race, physical abuse, suicide attempts, self-injurious behaviours, psychotropic medication, and psychiatric hospitalizations.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.019
Bibliometrics0.0060.007
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.293
Teacher spread0.282 · 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.

Study designMeta-analysis
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

Citations11
Published2021
Admission routes1
Has abstractyes

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Same venueEarly Intervention in PsychiatrySame topicBipolar Disorder and TreatmentFrench-language works237,207