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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".