Typology of clinical course in bipolar disorder based on 18-month naturalistic follow-up
Bibliographic record
Abstract
BACKGROUND: Individual variation in the clinical course of bipolar disorder may have prognostic and therapeutic implications but is poorly reflected in current classifications. We aimed to establish a typology of the individual clinical trajectories based on detailed prospective medium-term follow-up. Method Latent class analysis (LCA) of nine characteristics of clinical course (time depressed, severity of depression, stability of depression, time manic, severity of mania, stability of mania, mixed symptoms, mania-to-depression and depression-to-mania phase switching) derived from life charts prospectively tracking the onsets and offsets of (hypo)manic, depressive, mixed and subsyndromal episodes in a representative sample of 176 patients with bipolar disorder. RESULTS: The best-fitting model separated patients with bipolar disorder into large classes of episodic bipolar (47%) and depressive type (32%), moderately sized classes characterized by prolonged hypomanias (10%) and mixed episodes (5%) and five small classes with unusual course characteristics including mania-to-depression and depression-to-mania transitions and chronic mixed affective symptoms. This empirical typology is relatively independent of the distinction between bipolar disorder type I and type II. Lifetime co-morbidity of alcohol use disorders is characteristic of the episodic bipolar course type. CONCLUSIONS: There is potential for a new typology of clinical course based on medium-term naturalistic follow-up of a representative clinical sample of patients with bipolar disorder. Predictive validity and stability over longer follow-up periods remain to be established.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".