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Record W4387674960 · doi:10.1176/appi.focus.20230011

Suicide Assessment and Prevention in Bipolar Disorder: How Current Evidence Can Inform Clinical Practice

2023· article· en· W4387674960 on OpenAlexaff
Niloofar Izadi, Rachel Mitchell, Peter Giacobbe, Sean M. Nestor, Rosalie Steinberg, Jasmine Amini, Mark Sinyor, Ayal Schaffer

Bibliographic record

VenueFOCUS The Journal of Lifelong Learning in Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsBipolar disorderPsychological interventionPsychiatryPsychologyMedicinePrevalence of mental disordersClinical psychologyMental healthMood

Abstract

fetched live from OpenAlex

Bipolar disorder is associated with a considerable risk of suicide, and this fact must be incorporated into management of all patients with the condition. This article highlights the importance of a more nuanced understanding of the factors associated with the increased risk of suicidal behavior in people diagnosed as having bipolar disorder and interventions that could mitigate it. Several sociodemographic, clinical, environmental, and other variables have been associated with suicide attempts or deaths in bipolar disorder. Youths with bipolar disorder are a particularly vulnerable group, and their trajectory of illness could be modified by early interventions. Several medications have been studied regarding their relationship to suicide risk in bipolar disorder, and interventional psychiatry is a newer area of research focus. Finally, community-based approaches can be incorporated into a comprehensive approach to suicide prevention. This article summarizes the current understanding of key variables that can help inform a clinical risk assessment of individuals and interventions that can be employed in suicide prevention in bipolar disorder.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.181
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.050
GPT teacher head0.421
Teacher spread0.371 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFOCUS The Journal of Lifelong Learning in PsychiatrySame topicBipolar Disorder and TreatmentFrench-language works237,207