Clinical course predicts long-term outcomes in bipolar disorder
Bibliographic record
Abstract
BACKGROUND: The long-term outcomes of bipolar disorder range from lasting remission to chronic course or frequent recurrences requiring admissions. The distinction between bipolar I and II disorders has limited utility in outcome prediction. It is unclear to what extent the clinical course of bipolar disorder predicts long-term outcomes. METHODS: A representative sample of 191 individuals diagnosed with bipolar I or II disorder was recruited and followed for up to 5 years using a life-chart method. We previously described the clinical course over the first 18 months with dimensional course characteristics and latent classes. Now we test if these course characteristics predict long-term outcomes, including time ill (time with any mood symptoms) and hospital admissions over a second non-overlapping follow-up period in 111 individuals with available data from both 18 months and 5 years follow-ups. RESULTS: Dimensional course characteristics from the first 18 months prospectively predicted outcomes over the following 3.5 years. The proportion of time depressed, the severity of depressive symptoms and the proportion of time manic predicted more time ill. The proportion of time manic, the severity of manic symptoms and depression-to-mania switching predicted a greater likelihood of hospital admissions. All predictions remained significant after controlling for age, sex and bipolar I v. II disorder. CONCLUSIONS: Differential associations with long-term outcomes suggest that course characteristics may facilitate care planning with greater predictive validity than established types of bipolar disorders. A clinical course dominated by depressive symptoms predicts a greater proportion of time ill. A clinical course characterized by manic episodes predicts hospital admissions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".