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Record W2019461236 · doi:10.1002/pmh.13

The impact of personality disorders on treatment outcome in bipolar disorder: A review

2007· review· en· W2019461236 on OpenAlexaff
Peter Bieling, Sheryl M. Green, Glenda MacQueen

Bibliographic record

VenuePersonality and Mental Health · 2007
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsBipolar disorderPersonality disordersComorbidityPsychopathologyPsychologyClinical psychologyPsychiatryBorderline personality disorderPersonalityCognition

Abstract

fetched live from OpenAlex

Abstract Bipolar disorder (BD) is a chronic psychiatric illness for which there are a number of efficacious and effective treatments. However, for many sufferers recovery is incomplete or tenuous. Factors associated with poor outcomes in the disorder are of special interest, and comorbidity of BD with personality disorder (PD) has been proposed as a possible predictor of poor outcome. We reviewed available studies (n = 12) in the literature that specifically assessed the impact of personality psychopathology on illness outcomes in BD including functioning, response to treatment and suicidality. Quality of methodology, assessment methods and number of participants in studies were highly variable. Despite these variations in study quality, the presence of a PD was robustly associated (usually medium size effects) with a worse outcome in BD. Patients with BD and a diagnosis of PD are more likely to be hospitalized, require more time to achieve symptom stabilization, have more chronic impairments in occupational and social functioning, are less compliant to medication, have greater levels of suicidality and utilize more psychiatric services than patients with BD alone. The implications of these findings for further research and clinical care in BD are discussed. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
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 head0.096
GPT teacher head0.458
Teacher spread0.362 · 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 designNot applicable
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

Citations12
Published2007
Admission routes1
Has abstractyes

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