Comparing Features of Bipolar Disorder to Major Depressive Disorder in a Tertiary Mood Disorders Clinic
Bibliographic record
Abstract
BACKGROUND: We sought to describe features that distinguish individuals with bipolar disorder from major depressive disorder. METHODS: A retrospective chart review of adult outpatients (N = 1000) seeking evaluation and treatment was conducted at the Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto between October 2002 and November 2005 was conducted. Sociodemographic parameters, illness-characteristics and therapeutic interventions were evaluated and compared. RESULTS: The MDPU referring diagnosis were major depressive disorder (52%), bipolar disorder (29%), and unspecified (19%). Of all individuals with a non-bipolar entry diagnosis (n = 699), 23% (n = 159) were subsequently diagnosed with bipolar disorder (p < 0.001); the majority of whom (n = 117, 74%) received a non-bipolar I disorder diagnosis [e.g. bipolar II disorder (n = 71); bipolar NOS disorder (n = 46) (p < 0.001)]. Higher rates of unemployment/disability, previous depressive episodes, psychiatric hospitalization, comorbid hypertension, and lifetime substance use disorders, as well as an earlier age of illness-onset were more frequently endorsed by individuals with a diagnosis of bipolar disorder. Fifteen percent of individuals who were newly-diagnosed with bipolar disorder reported a history of antidepressant-associated mania. CONCLUSIONS: The majority of individuals with a newly-diagnosed bipolar disorder at this tertiary center have a non-bipolar I disorder (i.e., bipolar spectrum). Several indices of illness severity differentiate individuals with bipolar disorder from major depressive disorder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".