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Record W4386383516 · doi:10.4088/jcp.22m14693

Risk-Sensitive Decision-Making and Self-Harm in Youth Bipolar Disorder

2023· article· en· W4386383516 on OpenAlexafffund
Mikaela K. Dimick, Alysha A. Sultan, Kody G. Kennedy, Sakina J. Rizvi, Erika E. Forbes, Mark Sinyor, Roger S. McIntyre, Eric A. Youngstrom, Benjamin I. Goldstein

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2023
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsHealth Sciences CentreUniversity Health NetworkUniversity of TorontoSt. Michael's HospitalSunnybrook Health Science CentreCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsBipolar disorderHarmPsychologyPsychiatryDepression (economics)Clinical psychologySuicide preventionPoison controlMedicineMedical emergencyCognitionSocial psychology

Abstract

fetched live from OpenAlex

Youth with bipolar disorder (BD) are at high risk for suicide and have high rates of self-harm, which includes both suicide attempts and non-suicidal self-injury. Greater risk-taking has been associated with suicide attempts in youth with major depression, although there are no studies examining the relationship between risk-related decision-making and self-harm in youth with BD. We aimed to examine the association of suicide risk with risk-sensitive decision-making in a controlled sample of youth with BD. ]) and 82 age- and sex-matched control youth aged 13-20 years were recruited between 2012 and 2020. Decision-making and risk-taking performance were assessed via the Cambridge Gambling Task within the Cambridge Neuropsychological Test Automated Battery (CANTAB). General linear models were used to examine differences between groups with control for age, sex, and IQ. = .01). youth serving a protective role in suicide risk. Future longitudinal studies are needed to examine the temporal association of neurocognition and self-harm among youth with BD.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.413
Teacher spread0.363 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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