Personality disorders as predictors for the conversion from major depressive disorder to bipolar disorder: A prospective cohort study
Bibliographic record
Abstract
Studies suggest that personality disorders can predict mood disorders. The present study aims to assess whether personality traits in individuals with major depressive disorder (MDD) can predict the conversion to bipolar disorder (BD). This was a prospective cohort study conducted in two waves. In the first wave, 585 subjects were diagnosed with MDD; in the second wave, all of them were reevaluated in a three-year follow-up. Personality traits were evaluated, in the first phase, using the Millon Clinical Multiaxial Inventory (MCMI). MDD and BD diagnosis was performed by trained psychologists using a clinical structured interview based on diagnostics criteria of DSM IV, the Mini International Neuropsychiatric Interview, Plus version (MINI-PLUS). During the second wave, 468 individuals were reevaluated. Diagnostic conversion rate from MDD to BD was 12.4%. Higher mean scores in Antissocial, Borderline personality traits and in the sum of all Cluster B disorders were found among individuals who had converted to BD. In addition, individuals who converted to BD had lowest scores in obsessive-compulsive personality traits. Our findings suggest that Cluster B personality disorders can be considered as predictors of diagnostics conversion from MDD to BD. Also, it seems that obsessive-compulsive traits were lower among those individuals who have converted to BD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".