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Longitudinal outcome in patients with bipolar disorder assessed by life‐charting is influenced by DSM‐IV personality disorder symptoms

2003· article· en· W1968046919 on OpenAlexaff
Peter Bieling, Glenda MacQueen, Michael J Marriot, Janine Robb, Helen Bégin, Russell T. Joffe, L. Trevor Young

Bibliographic record

VenueBipolar Disorders · 2003
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsPersonality pathologyPsychosocialPersonalityBipolar disorderPsychologyPersonality disordersClinical psychologyPsychological interventionPsychiatryBipolar II disorderBorderline personality disorderDSM-5Mood

Abstract

fetched live from OpenAlex

OBJECTIVES: Few studies have examined the question of how personality features impact outcome in bipolar disorder (BD), though results from extant work and studies in major depressive disorder suggest that personality features are important in predicting outcome. The primary purpose of this paper was to examine the impact of DSM-IV personality disorder symptoms on long-term clinical outcome in BD. METHODS: The study used a 'life-charting' approach in which 87 BD patients were followed regularly and treated according to published guidelines. Outcome was determined by examining symptoms over the most recent year of follow-up and personality symptoms were assessed with the Structured Clinical Interview for DSM-IV (SCID-II) instrument at entry into the life-charting study. RESULTS: Patients with better outcomes had fewer personality disorder symptoms in seven out of 10 disorder categories and Cluster A personality disorder symptoms best distinguished euthymic and symptomatic patients. CONCLUSIONS: These results raise important questions about the mechanisms linking personality pathology and outcome in BD, and argue that conceptual models concerning personality pathology and BD need to be further developed. Treatment implications of our results, such as need for psychosocial interventions and treatment algorithms, are also described.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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 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

Citations51
Published2003
Admission routes1
Has abstractyes

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