Longitudinal outcome in patients with bipolar disorder assessed by life‐charting is influenced by DSM‐IV personality disorder symptoms
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".